finite difference immersed boundary method consistent with wall conditions for incompressible turbulent flow simulations an immersed boundary method to achieve the consistency with a desired wall velocity was developed existing schemes of immersed boundary methods for incompressible flow violate the wall condition in the discrete equation system during time advancement this problem arises from the inconsistency of the pressure with the velocity interpolated to represent the solid wall which does not coincide with the computational grid the numerical discrepancy does not become evident in the laminar flow simulation but in the turbulent flow simulation to eliminate this inconsistency a modified pressure equation based on the interpolated pressure gradient was derived for the spatial second order discrete equation system the conservation of the wall condition mass momentum and energy in the present method was theoretically demonstrated to verify the theory large eddy simulations for a plane channel circular pipe and nuclear rod bundle were successfully performed both these theoretical and numerical validations improve the reliability and the applicability of the immersed boundary method

exact penalty results for mathematical programs with vanishing constraints a mathematical program with vanishing constraints mpvc is a constrained optimization problem arising in certain engineering applications the feasible set has a complicated structure so that the most familiar constraint qualifications are usually violated this in turn implies that standard penalty functions are typically non exact for mpvcs we therefore develop a new mpvc tailored penalty function which is shown to be exact under reasonable assumptions this new penalty function can then be used to derive or recover suitable optimality conditions for mpvcs

aggregating local image descriptors into compact codes this paper addresses the problem of large scale image search three constraints have to be taken into account search accuracy efficiency and memory usage we first present and evaluate different ways of aggregating local image descriptors into a vector and show that the fisher kernel achieves better performance than the reference bag of visual words approach for any given vector dimension we then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation the evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy searching a 100 million image data set takes about 250 ms on one processor core

towards reversal invariant image representation state of the art image classification approaches are mainly based on robust image representation such as the bag of features bof model or the convolutional neural network cnn architecture in real applications the orientation left right of an image or an object might vary from sample to sample whereas some handcrafted descriptors e g sift and network operations e g convolution are not reversal invariant leading to the unsatisfied stability of image features extracted from these models to deal with a popular solution is to augment the dataset by adding a left right reversed copy for each image this strategy improves the recognition accuracy to some extent but also brings the price of almost doubled time and memory consumptions on both the training and testing stages in this paper we present an alternative solution based on designing reversal invariant representation of local patterns so that we can obtain the identical representation for an image and its left right reversed copy for the bof model we design a reversal invariant version of sift descriptor named max sift a generalized ride algorithm which can be applied to a large family of local descriptors for the cnn architecture we present a simple idea of generating reversal invariant deep features ri deep and inspired by which design reversal invariant convolution ri conv layers to increase the cnn capacity without increasing the model complexity experiments reveal consistent accuracy gain on various image classification tasks including scene understanding fine grained object recognition and large scale visual recognition

real time superpixel segmentation by dbscan clustering algorithm in this paper we propose a real time image superpixel segmentation method with 50 frames s by using the density based spatial clustering of applications with noise dbscan algorithm in order to decrease the computational costs of superpixel algorithms we adopt a fast two step framework in the first clustering stage the dbscan algorithm with color similarity and geometric restrictions is used to rapidly cluster the pixels and then small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage a robust and simple distance function is defined for obtaining better superpixels in these two steps the experimental results demonstrate that our real time superpixel algorithm 50 frames s by the dbscan clustering outperforms the state of the art superpixel segmentation methods in terms of both accuracy and efficiency

generalized pooling for robust object tracking feature pooling in a majority of sparse coding based tracking algorithms computes final feature vectors only by low order statistics or extreme responses of sparse codes the high order statistics and the correlations between responses to different dictionary items are neglected we present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to model the statistical distribution of sparse codes since immediate matching between two distributions usually requires high computational costs we introduce the fisher vector to derive a more compact and discriminative representation for sparse codes of the visual target we encode target patches by local coordinate coding utilize gaussian mixture model to compute fisher vectors and finally train semi supervised linear kernel classifiers for visual tracking in order to handle the drifting problem during the tracking process these classifiers are updated online with current tracking results the experimental results on two challenging tracking benchmarks demonstrate that the proposed approach achieves a better performance than the state of the art tracking algorithms

super normal vector for human activity recognition with depth cameras the advent of cost effectiveness and easy operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition this paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras we extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information we then propose a general scheme of super normal vector snv to aggregate the low level polynormals into a discriminative representation which can be viewed as a simplified version of the fisher kernel representation in order to globally capture the spatial layout and temporal order an adaptive spatio temporal pyramid is introduced to subdivide a depth video into a set of space time cells in the extensive experiments the proposed approach achieves superior performance to the state of the art methods on the four public benchmark datasets i e msraction3d msrdailyactivity3d msrgesture3d and msractionpairs3d

a new class of functions for measuring solution integrality in the feasibility pump approach mixed integer optimization is a powerful tool for modeling many optimization problems arising from real world applications finding a first feasible solution represents the first step for several mixed integer programming mip solvers the feasibility pump is a heuristic for finding feasible solutions to mixed integer linear programming milp problems which is effective even when dealing with hard mip instances in this work we start by interpreting the feasibility pump as a frank wolfe method applied to a nonsmooth concave merit function then we define a general class of functions that can be included in the feasibility pump scheme for measuring solution integrality and we identify some merit functions belonging to this class we further extend our approach by dynamically combining two different merit functions finally we define a new version of the feasibility pump algorithm which includes the original version of the feasibility pump as a special case and we present computational results on bi

a robust and efficient video representation for action recognition this paper introduces a state of the art video representation and applies it to efficient action recognition and detection we first propose to improve the popular dense trajectory features by explicit camera motion estimation more specifically we extract feature point matches between frames using surf descriptors and dense optical flow the matches are used to estimate a homography with ransac to improve the robustness of homography estimation a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera trajectories consistent with the homography are considered as due to camera motion and thus removed we also use the homography to cancel out camera motion from the optical flow this results in significant improvement on motion based hof and mbh descriptors we further explore the recent fisher vector as an alternative feature encoding approach to the standard bag of words bow histogram and consider different ways to include spatial layout information in these encodings we present a large and varied set of evaluations considering i classification of short basic actions on six datasets ii localization of such actions in feature length movies and iii large scale recognition of complex events we find that our improved trajectory features significantly outperform previous dense trajectories and that fisher vectors are superior to bow encodings for video recognition tasks in all three tasks we show substantial improvements over the state of the art results

large eddy simulation in complex geometric configurations using boundary body forces anumericalmethodispresentedthatallowslargeeddysimulation les ofturbulente owsincomplexgeometric cone gurations with moving boundaries and that retains the advantages of solving the navier stokes equations on e xed orthogonal grids the boundary conditions are applied independently of the grid by assigning body forces over surfaces that need not coincide with coordinate lines the use of orthogonal nondeforming grids simplie es grid generation facilitates theimplementation of high order nondissipativediscretization schemes andminimizes the spatial and temporal variations in e lter width that complicate unstructured deforming grid les dynamic subgrid scaleturbulence models areparticularly appealing in combination with the body forceprocedure because the dynamic model accounts automatically for the presence of solid walls without requiring damping functions the method is validated by simulations of the turbulent e ow in a motored axisymmetric piston cylinder assembly for which detailed experimental measurements are available computed mean and rms velocity proe les show very good agreement with measured ensemble averages thepresent numerical code runs on small personal computerlike workstations for a comparable level of accuracy computational requirements memory and cpu time are at least a factor of 10 lower compared to published simulations for the same cone guration obtained using an unstructured boundary e tted deforming grid approach

direct simulations of turbulent flow using finite difference schemes abstract this paper presents finite difference solutions to the evolution of small amplitude disturbances and incompressible fully developed turbulent channel flow the main objective of the paper is to provide a comprehensive comparison between the results obtained using finite difference and spectral methods an advantage of finite difference schemes over the highly accurate spectral methods lies in the ease with which they can be applied to complex geometries the finite difference methods used include a kinetic energy conserving type of central difference scheme and a high order accurate upwind difference scheme unlike the central difference scheme the upwind difference scheme was found not to require a kinetic energy conservation property to control aliasing error the dissipative nature of the upwind scheme results in a damping of the higher frequency content as a result very little energy is aliased back the computed data including first and second order statistics for the turbulent channel flow case are found to compare well with experimental data and earlier spectral simulations it appears that the high order accurate upwind scheme is a good candidate for direct simulations of turbulent flows over complex geometries

boosted random contextual semantic space based representation for visual recognition we propose an image representation method using boosted random contextual semantic spaces the proposed brcss can alleviate the semantic gap by using semantic space based image representation brcss uses the visual representation of images in an iterative way with random sampling and re weighting experimental results demonstrate the effectiveness and efficiency of the proposed method visual information has been widely used for image representation although proven very effective the visual representation lacks explicit semantics however how to generate a proper semantic space for image representation is still an open problem that needs to be solved to jointly model the visual and semantic representations of images we propose a boosted random contextual semantic space based image representation method images are initially represented using local feature s distribution histograms the semantic space is generated by randomly selecting training images images are then mapped into the semantic space accordingly semantic context is explored to model the correlations of different semantics which is then used for classification the classification results are used to re weight training images in a boosted way the re weighted images are used to construct new semantic space for classification in this way we are able to jointly consider the visual and semantic information of images image classification experiments on several public datasets show the effectiveness of the proposed method

estimation of ergodicity limits of bag of words modeling for guaranteed stochastic convergence abstract this paper suggests an efficient dual ergodicity limits based bag of words del bow modeling technique the suggested del bow technique estimates two limits of ergodicity of a discrete random variable drv that is formed from the bow classification performance of multiple runs the first limit of ergodicity is estimated with a relatively larger ball of convergence to keep the drv shorter hence both robustness against random initialization and estimation of the optimal model order are realized with a reduced number of iterations once the optimal model order is estimated the radius of ball of convergence is reduced and a second limit of ergodicity is estimated reducing the ball of convergence enlarges the size of the considered performance drv that enhances the classification performance experiments conducted on caltech 101 caltech 256 15 scenes and flower 102 datasets resulted in classification accuracy of 86 91 72 57 90 57 and 90 86 respectively comparison with state of the art techniques shows the excellent performance of the del bow modeling process

on an exact penalty function method for nonlinear mixed discrete programming problems and its applications in search engine advertising problems in this paper we study a new exact and smooth penalty function for the nonlinear mixed discrete programming problem by augumenting only one variable no matter how many constraints through the smooth and exact penalty function we can transform the nonlinear mixed discrete programming problem into an unconstrained optimization model we demonstrate that under mild conditions when the penalty parameter is sufficiently large optimizers of this penalty function are precisely the optimizers of the nonlinear mixed discrete programming problem alternatively under some mild assumptions the local exactness property is also presented the numerical results demonstrate that the new penalty function is an effective and promising approach as important applications we solve an increasingly popular search engine advertising problem via the new proposed penalty function

a norm selection criterion for the generalized delta rule the derivation of a supervised training algorithm for a neural network implies the selection of a norm criterion which gives a suitable global measure of the particular distribution of errors the author addresses this problem and proposes a correspondence between error distribution at the output of a layered feedforward neural network and l sub p norms the generalized delta rule is investigated in order to verify how its structure can be modified in order to perform a minimization in the generic l sub p norm the particular case of the chebyshev norm is developed and tested

evaluation of ground distances and features in emd based gmm matching for texture classification recently the earth mover s distance emd has demonstrated its superiority in gaussian mixture models gmms based texture classification the ground distances between gaussian components of gmms have great influences on performance of gmm matching which however has not been fully studied yet meanwhile image features play a key role in image classification task and often greatly impact classification performance in this paper we present a comprehensive study of ground distances and image features in texture classification task we divide existing ground distances into statistics based ones and riemannian manifold based ones we make a theoretical analysis of the differences and relationships among these ground distances inspired by gaussian embedding distance and product of lie groups distance we propose an improved gaussian embedding distance to compare gaussians we also evaluate for the first time the image features for gmm matching including the handcrafted features such as gabor filter local binary pattern lbp descriptor sift covariance descriptor and high level features extracted by deep convolution networks the experiments are conducted on three texture databases i e kth tips 2b fmd and uiuc based on experimental results we show that the uses of geometrical structure and balance strategy are critical to ground distances the experimental results show that gmm with the proposed ground distance can achieve state of the art performance when high level features are exploited highlightswe present a comprehensive study of ground distances and image features in emd based gmm matching for texture classification an improved gaussian embedding distance is proposed to compare gaussians the experimental results show that our method can achieve state of the art performance

towards effective codebookless model for image classification the bag of features bof model for image classification has been thoroughly studied over the last decade different from the widely used bof methods which model images with a pre trained codebook the alternative codebook free image modeling method which we call codebookless model clm attracts little attention in this paper we present an effective clm that represents an image with a single gaussian for classification by embedding gaussian manifold into a vector space we show that the simple incorporation of our clm into a linear classifier achieves very competitive accuracy compared with state of the art bof methods e g fisher vector since our clm lies in a high dimensional riemannian manifold we further propose a joint learning method of low rank transformation with support vector machine svm classifier on the gaussian manifold in order to reduce computational and storage cost to study and alleviate the side effect of background clutter on our clm we also present a simple yet effective partial background removal method based on saliency detection experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our clm method highlightswe show that codebookless model clm is a very competitive alternative to the mainstream bof model two well motivated parameters are introduced to further improve our clm a joint low rank learning and svm is proposed on gaussian manifold the comprehensive experiments demonstrated the promising performance of our clm

on the accuracy of direct forcing immersed boundary methods with projection methods direct forcing methods are a class of methods for solving the navier stokes equations on nonrectangular domains the physical domain is embedded into a larger rectangular domain and the equations of motion are solved on this extended domain the boundary conditions are enforced by applying forces near the embedded boundaries this raises the question of how the flow outside the physical domain influences the flow inside the physical domain this question is particularly relevant when using a projection method for incompressible flow in this paper analysis and computational tests are presented that explore the performance of projection methods when used with direct forcing methods sufficient conditions for the success of projection methods on extended domains are derived and it is shown how forcing methods meet these conditions bounds on the error due to projecting on the extended domain are derived and it is shown that direct forcing methods are in general first order accurate in the max norm numerical tests of the projection alone confirm the analysis and show that this error is concentrated near the embedded boundaries leading to higher order accuracy in integral norms generically forcing methods generate a solution that is not smooth across the embedded boundaries and it is this lack of smoothness which limits the accuracy of the methods additional computational tests of the navier stokes equations involving a direct forcing method and a projection method are presented and the results are compared with the predictions of the analysis these results confirm that the lack of smoothness in the solution produces a lower order error the rate of convergence attained in practice depends on the type of forcing method used

fine grained image classification via low rank sparse coding with general and class specific codebooks this paper tries to separate fine grained images by jointly learning the encoding parameters and codebooks through low rank sparse coding lrsc with general and class specific codebook generation instead of treating each local feature independently we encode the local features within a spatial region jointly by lrsc this ensures that the spatially nearby local features with similar visual characters are encoded by correlated parameters in this way we can make the encoded parameters more consistent for fine grained image representation besides we also learn a general codebook and a number of class specific codebooks in combination with the encoding scheme since images of fine grained classes are visually similar the difference is relatively small between the general codebook and each class specific codebook we impose sparsity constraints to model this relationship moreover the incoherences with different codebooks and class specific codebooks are jointly considered we evaluate the proposed method on several public image data sets the experimental results show that by learning general and class specific codebooks with the joint encoding of local features we are able to model the differences among different fine grained classes than many other fine grained image classification methods

an immersed boundary finite volume method for simulations of flow in complex geometries abstract a new immersed boundary method for simulating flows over or inside complex geometries is developed by introducing a mass source sink as well as a momentum forcing the present method is based on a finite volume approach on a staggered mesh together with a fractional step method both momentum forcing and mass source are applied on the body surface or inside the body to satisfy the no slip boundary condition on the immersed boundary and also to satisfy the continuity for the cell containing the immersed boundary in the immersed boundary method the choice of an accurate interpolation scheme satisfying the no slip condition on the immersed boundary is important because the grid lines generally do not coincide with the immersed boundary therefore a stable second order interpolation scheme for evaluating the momentum forcing on the body surface or inside the body is proposed three different flow problems decaying vortices and flows over a cylinder and a sphere are simulated using the immersed boundary method proposed in this study and the results agree very well with previous numerical and experimental results verifying the accuracy of the present method

object categorization using class specific representations object categorization refers to the task of automatically classifying objects based on the visual content existing approaches simply represent each image with the visual features without considering the specific characters of images within the same class however objects of the same class may exhibit unique characters which should be represented accordingly in this brief we propose a novel class specific representation strategy for object categorization for each class we first model the characters of images within the same class using gaussian mixture model gmm we then represent each image by calculating the euclidean distance and relative euclidean distance between the image and the gmm model for each class we concatenate the representations of each class for joint representation in this way we can represent an image by not only considering the visual contents but also combining the class specific characters experiments on several public available data sets validate the superiority of the proposed class specific representation method over well established algorithms for object category predictions

high order local pooling and encoding gaussians over a dictionary of gaussians local pooling lp in configuration feature space proposed by boureau et al explicitly restricts similar features to be aggregated which can preserve as much discriminative information as possible at the time it appeared this method combined with sparse coding achieved competitive classification results with only a small dictionary however its performance lags far behind the state of the art results as only the zero order information is exploited inspired by the success of high order statistical information in existing advanced feature coding or pooling methods we make an attempt to address the limitation of lp to this end we present a novel method called high order lp ho lp to leverage the information higher than the zero order one our idea is intuitively simple we compute the first and second order statistics per configuration bin and model them as a gaussian accordingly we employ a collection of gaussians as visual words to represent the universal probability distribution of features from all classes our problem is naturally formulated as encoding gaussians over a dictionary of gaussians as visual words this problem however is challenging since the space of gaussians is not a euclidean space but forms a riemannian manifold we address this challenge by mapping gaussians into the euclidean space which enables us to perform coding with common euclidean operations rather than complex and often expensive riemannian operations our ho lp preserves the advantages of the original lp pooling only similar features and using a small dictionary meanwhile it achieves very promising performance on standard benchmarks with either conventional hand engineered features or deep learning based features

data driven spatially adaptive metric adjustment for visual tracking matching visual appearances of the target over consecutive video frames is a fundamental yet challenging task in visual tracking its performance largely depends on the distance metric that determines the quality of visual matching rather than using fixed and predefined metric recent attempts of integrating metric learning based trackers have shown more robust and promising results as the learned metric can be more discriminative in general these global metric adjustment methods are computationally demanding in real time visual tracking tasks and they tend to underfit the data when the target exhibits dynamic appearance variation this paper presents a nonparametric data driven local metric adjustment method the proposed method finds a spatially adaptive metric that exhibits different properties at different locations in the feature space due to the differences of the data distribution in a local neighborhood it minimizes the deviation of the empirical misclassification probability to obtain the optimal metric such that the asymptotic error as if using an infinite set of training samples can be approximated moreover by taking the data local distribution into consideration it is spatially adaptive integrating this new local metric learning method into target tracking leads to efficient and robust tracking performance extensive experiments have demonstrated the superiority and effectiveness of the proposed tracking method in various tracking scenarios

image classification by search with explicitly and implicitly semantic representations we model the explicitly and implicitly semantic representations for image classification explicitly semantic representations are obtained using learned models with supervision implicitly semantic representations are obtained with information from the internet the explicitly and implicitly semantic based method achieves good performances image classification refers to the task of automatically classifying the categories of images based on the contents this task is typically solved using visual features with the histogram based classification scheme although effective this strategy has two drawbacks on one hand histogram based representation often disregards the object layout which is very important for classification on the other hand visual features are unable to fully separate different images due to the semantic gap to solve these two problems in this paper we propose a novel image classification method by explicitly and implicitly representing the images with searching strategy first to make use of object layouts we randomly select a number of regions and then use these regions for image representations second we generate the explicitly semantic representations using a number of pre learned semantic models third we measure the visual similarities with the internet images and use the text information for implicitly semantic representations since internet images are contaminated with noise the resulting representations only implicitly reflect the contents of images finally both the explicitly and implicitly semantic representations are jointly modeled for image classifications by training bi linear classifiers we evaluate the effectiveness of the proposed image classification by search with explicitly and implicitly semantic representations method eisr on the scene 15 dataset the mit indoor dataset the uiuc sports dataset and the pascal voc 2007 dataset the experimental results prove the usefulness of the proposed method

ensemble tracking we consider tracking as a binary classification problem where an ensemble of weak classifiers is trained online to distinguish between the object and the background the ensemble of weak classifiers is combined into a strong classifier using adaboost the strong classifier is then used to label pixels in the next frame as either belonging to the object or the background giving a confidence map the peak of the map and hence the new position of the object is found using mean shift temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained online during tracking we show a realization of this method and demonstrate it on several video sequences

efficient additive kernels via explicit feature maps large scale nonlinear support vector machines svms can be approximated by linear ones using a suitable feature map the linear svms are in general much faster to learn and evaluate test than the original nonlinear svms this work introduces explicit feature maps for the additive class of kernels such as the intersection hellinger s and 2 kernels commonly used in computer vision and enables their use in large scale problems in particular we 1 provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels 2 derive corresponding approximate finite dimensional feature maps based on a spectral analysis and 3 quantify the error of the approximation showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as 2 we demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train test times of svms we also compare with two other approximation methods nystrom s approximation of perronnin et al 1 which is data dependent and the explicit map of maji and berg 2 for the intersection kernel which as in the case of our approximations is data independent the approximations are evaluated on a number of standard data sets including caltech 101 3 daimler chrysler pedestrians 4 and inria pedestrians 5

training faster by separating modes of variation in batch normalized models batch normalization bn is essential to effectively train state of the art deep convolutional neural networks cnn it normalizes the layer outputs during training using the statistics of each mini batch bn accelerates training procedure by allowing to safely utilize large learning rates and alleviates the need for careful initialization of the parameters in this work we study bn from the viewpoint of fisher kernels that arise from generative probability models we show that assuming samples within a mini batch are from the same probability density function then bn is identical to the fisher vector of a gaussian distribution that means batch normalizing transform can be explained in terms of kernels that naturally emerge from the probability density function that models the generative process of the underlying data distribution consequently it promises higher discrimination power for the batch normalized mini batch however given the rectifying non linearities employed in cnn architectures distribution of the layer outputs show an asymmetric characteristic therefore in order for bn to fully benefit from the aforementioned properties we propose approximating underlying data distribution not with one but a mixture of gaussian densities deriving fisher vector for a gaussian mixture model gmm reveals that batch normalization can be improved by independently normalizing with respect to the statistics of disentangled sub populations we refer to our proposed soft piecewise version of batch normalization as mixture normalization mn through extensive set of experiments on cifar 10 and cifar 100 using both a 5 layers deep cnn and modern inception v3 architecture we show that mixture normalization reduces required number of gradient updates to reach the maximum test accuracy of the batch normalized model by sim 31 47 31 47 across a variety of training scenarios replacing even a few bn modules with mn in the 48 layers deep inception v3 architecture is sufficient to not only obtain considerable training acceleration but also better final test accuracy we show that similar observations are valid for 40 and 100 layers deep densenet architectures as well we complement our study by evaluating the application of mixture normalization to the generative adversarial networks gans where mode collapse hinders the training process we solely replace a few batch normalization layers in the generator with our proposed mixture normalization our experiments using deep convolutional gan dcgan on cifar 10 show that mixture normalized dcgan not only provides an acceleration of sim 58 58 but also reaches lower better frechet inception distance fid of 33 35 compared to 37 56 of its batch normalized counterpart

on the convergence of the exponential multiplier method for convex programming in this paper we analyze the exponential method of multipliers for convex constrained minimization problems which operates like the usual augmented lagrangian method except that it uses an exponential penalty function in place of the usual quadratic we also analyze a dual counterpart the entropy minimization algorithm which operates like the proximal minimization algorithm except that it uses a logarithmic entropy proximal term in place of a quadratic we strengthen substantially the available convergence results for these methods and we derive the convergence rate of these methods when applied to linear programs

local binary features for texture classification local binary patterns lbp have emerged as one of the most prominent and widely studied local texture descriptors truly a large number of lbp variants has been proposed to the point that it can become overwhelming to grasp their respective strengths and weaknesses and there is a need for a comprehensive study regarding the prominent lbp related strategies new types of descriptors based on multistage convolutional networks and deep learning have also emerged in different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well known texture datasets with differing classifiers and testing protocols and often not using the best sets of parameter values and multiple scales for the comparative methods very important aspects such as computational complexity and effects of poor image quality are often neglected in this paper we provide a systematic review of current lbp variants and propose a taxonomy to more clearly group the prominent alternatives merits and demerits of the various lbp features and their underlying connections are also analyzed we perform a large scale performance evaluation for texture classification empirically assessing forty texture features including thirty two recent most promising lbp variants and eight non lbp descriptors based on deep convolutional networks on thirteen widely used texture datasets the experiments are designed to measure their robustness against different classification challenges including changes in rotation scale illumination viewpoint number of classes different types of image degradation and computational complexity the best overall performance is obtained for the median robust extended local binary pattern mrelbp feature for textures with very large appearance variations fisher vector pooling of deep convolutional neural networks is clearly the best but at the cost of very high computational complexity the sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered highlightsa taxonomy and comprehensive survey of lbp variants characteristics of and connections between lbp variants are provided a comprehensive experimental evaluation of 32 lbp methods comparison of 32 lbp variants with 8 deep convnets features evaluation of robustness to rotation illumination scale and noise changes comparison of computational complexity of forty variants

large eddy simulation of a road vehicle with drag reduction devices the e ow around an idealized road vehicle at reynolds numbers up to 10 5 has been simulated using large eddy simulation the numerical technique is based on the immersed boundary approach which allows efe cient calculations to be carried out on a cartesian grid the effect of the reynolds number and the wake modie cations produced by drag reduction devices attached to the base have been analyzed and compared with available experimental data averaged drag coefe cient and mean velocity proe les are in good agreement with measurement the effect of subgrid scale modeling smagorinsky and dynamic model has also been studied

training with noise is equivalent to tikhonov regularization it is well known that the addition of noise to the input data of a neural network during training can in some circumstances lead to significant improvements in generalization performance previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function however the regularization term which involves second derivatives of the error function is not bounded below and so can lead to difficulties if used directly in a learning algorithm based on error minimization in this paper we show that for the purposes of network training the regularization term can be reduced to a positive semi definite form that involves only first derivatives of the network mapping for a sum of squares error function the regularization term belongs to the class of generalized tikhonov regularizers direct minimization of the regularized error function provides a practical alternative to training with noise

manifold regularized correlation object tracking in this paper we propose a manifold regularized correlation tracking method with augmented samples to make better use of the unlabeled data and the manifold structure of the sample space a manifold regularization based correlation filter is introduced which aims to assign similar labels to neighbor samples meanwhile the regression model is learned by exploiting the block circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions thus the final classifier in our method is trained with positive negative and unlabeled base samples which is a semisupervised learning framework a block optimization strategy is further introduced to learn a manifold regularization based correlation filter for efficient online tracking experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state of the art tracking approaches

local log euclidean multivariate gaussian descriptor and its application to image classification this paper presents a novel image descriptor to effectively characterize the local high order image statistics our work is inspired by the diffusion tensor imaging and the structure tensor method or covariance descriptor and motivated by popular distribution based descriptors such as sift and hog our idea is to associate one pixel with a multivariate gaussian distribution estimated in the neighborhood the challenge lies in that the space of gaussians is not a linear space but a riemannian manifold we show for the first time to our knowledge that the space of gaussians can be equipped with a lie group structure by defining a multiplication operation on this manifold and that it is isomorphic to a subgroup of the upper triangular matrix group furthermore we propose methods to embed this matrix group in the linear space which enables us to handle gaussians with euclidean operations rather than complicated riemannian operations the resulting descriptor called local log euclidean multivariate gaussian l 2 emg descriptor works well with low dimensional and high dimensional raw features moreover our descriptor is a continuous function of features without quantization which can model the first and second order statistics extensive experiments were conducted to evaluate thoroughly l 2 emg and the results showed that l 2 emg is very competitive with state of the art descriptors in image classification

on the consistency of the direct forcing method in the fractional step solution of the navier stokes equations in the present work the application of the direct forcing method to the fractional step solution of the navier stokes equations is analyzed in details the central topic of the study is the satisfaction of the boundary conditions in the presence of immersed bodies to this aim simple two dimensional problems with steady forcing and sharp edged surfaces are solved with a standard fractional step method it is shown that the direct forcing scheme is not able to satisfy the impenetrability condition this fact appears to be strictly related to the use of fractional step methods a mathematically based explanation is given suggesting that improvements can be obtained with the iterative solution of the irrotational part of the flow when spectral methods are used and local modification of the discrete differential operators are difficult to be implemented the results are then verified in two dimensional unsteady problems and three dimensional flows with moving smooth boundaries

an augmented lagrangian function with improved exactness properties in this paper we introduce a new exact augmented lagrangian function for the solution of general nonlinear programming problems for this lagrangian function a complete equivalence between its unconstrained minimization on an open set and the solution of the original constrained problem can be established under mild assumptions and without requiring the boundedness of the feasible set of the constrained problem moreover we describe an unconstrained algorithmic model which is globally convergent toward kkt pairs of the original constrained problem the algorithmic model can be endowed with a superlinear rate of convergence by a proper choice of the search direction in the unconstrained minimization without requiring strict complementarity

interferences in match kernels we consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector popular representations of this kind include the bag of visual words the fisher vector and the vlad when two such image representations are compared with the dot product the image to image similarity can be interpreted as a match kernel in match kernels one has to deal with interference i e with the fact that even if two descriptors are unrelated their matching score may contribute to the overall similarity we formalise this problem and propose two related solutions both aimed at equalising the individual contributions of the local descriptors in the final representation these methods modify the aggregation stage by including a set of per descriptor weights they differ by the objective function that is optimised to compute those weights the first is a democratisation strategy that aims at equalising the relative importance of each descriptor in the set comparison metric the second one involves equalising the match of a single descriptor to the aggregated vector these concurrent methods give a substantial performance boost over the state of the art in image search with short or mid size vectors as demonstrated by our experiments on standard public image retrieval benchmarks

exact penalty functions method for mathematical programming problems involving invex functions in this paper some new results on the exact penalty function method are presented simple optimality characterizations are given for the differentiable nonconvex optimization problems with both inequality and equality constraints via exact penalty function method the equivalence between sets of optimal solutions in the original mathematical programming problem and its associated exact penalized optimization problem is established under suitable invexity assumption furthermore the equivalence between a saddle point in the invex mathematical programming problem and an optimal point in its exact penalized optimization problem is also proved

natural gradient works efficiently in learning when a parameter space has a certain underlying structure the ordinary gradient of a function does not represent its steepest direction but the natural gradient does information geometry is used for calculating the natural gradients in the parameter space of perceptrons the space of matrices for blind source separation and the space of linear dynamical systems for blind source deconvolution the dynamical behavior of natural gradient online learning is analyzed and is proved to be fisher efficient implying that it has asymptotically the same performance as the optimal batch estimation of parameters this suggests that the plateau phenomenon which appears in the backpropagation learning algorithm of multilayer perceptrons might disappear or might not be so serious when the natural gradient is used an adaptive method of updating the learning rate is proposed and analyzed

learning dictionary of discriminative part detectors for image categorization and cosegmentation this paper proposes a novel approach to learning mid level image models for image categorization and cosegmentation we represent each image class by a dictionary of part detectors that best discriminate that class from the background we learn category specific part detectors in a weakly supervised setting in which the training images are only annotated with category labels without part object location information we use a latent svm model regularized using the ell 2 1 l2 1 group sparsity norm to learn the part detectors starting from a large set of initial parts the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max margin framework we propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem we apply the learned part detectors to image classification and cosegmentation and present extensive comparative experiments with standard benchmarks

higher order energies for image segmentation a novel energy minimization method for general higher order binary energy functions is proposed in this paper we first relax a discrete higher order function to a continuous one and use the taylor expansion to obtain an approximate lower order function which is optimized by the quadratic pseudo boolean optimization or other discrete optimizers the minimum solution of this lower order function is then used as a new local point where we expand the original higher order energy function again our algorithm does not restrict to any specific form of the higher order binary function or bring in extra auxiliary variables for concreteness we show an application of segmentation with the appearance entropy which is efficiently solved by our method experimental results demonstrate that our method outperforms the state of the art methods

image classification with the fisher vector theory and practice a standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors encode them into a high dimensional vector and pool them into an image level signature the most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements this leads to the popular bag of visual words representation in this work we propose to use the fisher kernel framework as an alternative patch encoding strategy we describe patches by their deviation from an universal generative gaussian mixture model this representation which we call fisher vector has many advantages it is efficient to compute it leads to excellent results even with efficient linear classifiers and it can be compressed with a minimal loss of accuracy using product quantization we report experimental results on five standard datasets pascal voc 2007 caltech 256 sun 397 ilsvrc 2010 and imagenet10k with up to 9m images and 10k classes showing that the fv framework is a state of the art patch encoding technique

weakly supervised fine grained categorization with part based image representation in this paper we propose a fine grained image categorization system with easy deployment we do not use any object part annotation weakly supervised in the training or in the testing stage but only class labels for training images fine grained image categorization aims to classify objects with only subtle distinctions e g two breeds of dogs that look alike most existing works heavily rely on object part detectors to build the correspondence between object parts which require accurate object or object part annotations at least for training images the need for expensive object annotations prevents the wide usage of these methods instead we propose to generate multi scale part proposals from object proposals select useful part proposals and use them to compute a global image representation for categorization this is specially designed for the weakly supervised fine grained categorization task because useful parts have been shown to play a critical role in existing annotation dependent works but accurate part detectors are hard to acquire with the proposed image representation we can further detect and visualize the key most discriminative parts in objects of different classes in the experiments the proposed weakly supervised method achieves comparable or better accuracy than the state of the art weakly supervised methods and most existing annotation dependent methods on three challenging datasets its success suggests that it is not always necessary to learn expensive object part detectors in fine grained image categorization

hierarchical projective invariant contexts for shape recognition shape descriptors play an important role in various computer vision tasks many existing descriptors are typically derived from pair wise measures such as distances and angles which may vary with severe geometrical deformations including affine and projective transformations in this paper we propose a new shape descriptor from a newly developed projective invariant the characteristic number cn this new descriptor is invariant to projective or perspective transformations by computing cn values on a series of 5 sample points along the shape contour with the intervals varying from coarse to fine this hierarchical strategy yields a compacter descriptor so that the time complexity for both descriptor construction and shape matching are less or comparable to many existing methods we also use the derived points out of the contour and the ratio of two invariant values in order to improve the stability at finer scales and robustness to noise we demonstrate the performance of the descriptor by comparing with the state of the art on the mcd and other public shape sets with severe perspective transformations and other type variations including noise missing parts and articulated deformations graphical abstract highlightswe propose a new shape descriptor hcnc invariant to projective deformations hcnc is compact with a hierarchical strategy rendering efficient matching hcnc is robust to noise part missing and articulated deformations

adaptive metric learning for saliency detection in this paper we propose a novel adaptive metric learning algorithm aml for visual saliency detection a key observation is that the saliency of a superpixel can be estimated by the distance from the most certain foreground and background seeds instead of measuring distance on the euclidean space we present a learning method based on two complementary mahalanobis distance metrics 1 generic metric learning gml and 2 specific metric learning sml gml aims at the global distribution of the whole training set while sml considers the specific structure of a single image considering that multiple similarity measures from different views may enhance the relevant information and alleviate the irrelevant one we try to fuse the gml and sml together and experimentally find the combining result does work well different from the most existing methods which are directly based on low level features we devise a superpixelwise fisher vector coding approach to better distinguish salient objects from the background we also propose an accurate seeds selection mechanism and exploit contextual and multiscale information when constructing the final saliency map experimental results on various image sets show that the proposed aml performs favorably against the state of the arts

structured weak semantic space construction for visual categorization visual features have been widely used for image representation and categorization however visual features are often inconsistent with human perception besides constructing explicit semantic space is still an open problem to alleviate these two problems in this paper we propose to construct structured weak semantic space for image representation exemplar classifier is first trained to separate each training image from other images for weak semantic space construction however each exemplar classifier separates one training image from other images and it only has limited semantic separability besides the outputs of exemplar classifiers are inconsistent with each other we jointly construct the weak semantic space using structured constraint this is achieved by imposing low rank constraint on the outputs of exemplar classifiers with sparsity constraint an alternative optimization procedure is used to learn the exemplar classifiers since the proposed method does not dependent on the initial image representation strategy we can make use of various visual features for efficient exemplar classifier training e g fisher vector based methods and convolutional neural networks based methods we apply the proposed structured weak semantic space based image representation method for categorization the experimental results on several public image data sets prove the effectiveness of the proposed method

an exact penalty function method with global convergence properties for nonlinear programming problems in this paper a new continuously differentiable exact penalty function is introduced for the solution of nonlinear programming problems with compact feasible set a distinguishing feature of the penalty function is that it is defined on a suitable bounded open set containing the feasible region and that it goes to infinity on the boundary of this set this allows the construction of an implementable unconstrained minimization algorithm whose global convergence towards kuhn tucker points of the constrained problem can be established

product quantization for nearest neighbor search this paper introduces a product quantization based approach for approximate nearest neighbor search the idea is to decompose the space into a cartesian product of low dimensional subspaces and to quantize each subspace separately a vector is represented by a short code composed of its subspace quantization indices the euclidean distance between two vectors can be efficiently estimated from their codes an asymmetric version increases precision as it computes the approximate distance between a vector and a code experimental results show that our approach searches for nearest neighbors efficiently in particular in combination with an inverted file system results for sift and gist image descriptors show excellent search accuracy outperforming three state of the art approaches the scalability of our approach is validated on a data set of two billion vectors

a robust approach for object matching and classification using partial dominant orientation descriptor this paper introduces a novel approach to measure the correspondence between objects and exploit it for object and image classification tasks using the proposed partial dominant orientation descriptor pdod in particular the object is represented by a set of stable and informative key locations sampled using difference of gaussian the proposed pdod at each extracted key location takes into account the position and partially computes the dominant orientation of other key locations relative to it thus offering a global distinctive and discriminative characterization this allows us to learn features that are largely invariant to common image transformations including changes in object colors and textures the correspondence in between two objects is performed by finding for each key location in one object the key location in the other object that has the most similar descriptor object classification proceeds by assigning the most relevant category that has maximally similar stored prototype objects to the query object using k nearest neighbors algorithm with adaptive object distance for efficiency we further investigate pdod for image classification by developing powerful image representations based on the popular bag of words model the extensive experiments demonstrate that the proposed approach greatly improves the matching and classification results while achieving the state of the art performances on several challenging benchmark datasets the obtained results suggest also broader applicability to other classification modalities we propose a novel descriptor for complex object matching and classification the proposed descriptor combines both local and global properties of interest points the proposed descriptor is highly distinctive and insensitive to common image transformations adaptive object distance is proposed to measure the similarity between objects we compare our approach with many existing methods and we achieve the state of the art performances on several benchmarks

best fitting hyperplanes for classification in this paper we propose novel methods that are more suitable than classical large margin classifiers for open set recognition and object detection tasks the proposed methods use the best fitting hyperplanes approach and the main idea is to find the best fitting hyperplanes such that each hyperplane is close to the samples of one of the classes and is as far as possible from the other class samples to this end we propose two different classifiers the first classifier solves a convex quadratic optimization problem but negative samples can lie on one side of the best fitting hyperplane the second classifier however allows the negative samples to lie on both sides of the fitting hyperplane by using concave convex procedure both methods are extended to the nonlinear case by using the kernel trick in contrast to the existing hyperplane fitting classifiers in the literature our proposed methods are suitable for large scale problems and they return sparse solutions the experiments on several databases show that the proposed methods typically outperform other hyperplane fitting classifiers and they work as good as the svm classifier in classical recognition tasks however the proposed methods significantly outperform svm in open set recognition and object detection tasks

image level classification by hierarchical structure learning with visual and semantic similarities abstract image classification methods often use class level information without considering the distinctive character of each image images of the same class may have varied appearances besides visually similar images may not be semantically correlated to solve these problems in this paper we propose a novel image classification method by automatically learning the image level hierarchical structure ilhs using both visual and semantic similarities we try to generate new representations by exploring both visual and semantic similarities of images images are clustered hierarchically to explore their correlations we then use them for image representations the diversity of image classes within each cluster is used to re weight visual similarities the re weighted similarities are aggregated to generate new image representations we conduct image classification experiments on the caltech 256 dataset the pascal voc 2007 dataset and the pascal voc 2012 dataset experimental results demonstrate the effectiveness of the proposed method

compact representation of high dimensional feature vectors for large scale image recognition and retrieval in large scale visual recognition and image retrieval tasks feature vectors such as fisher vector fv or the vector of locally aggregated descriptors vlad have achieved state of the art results however the combination of the large numbers of examples and high dimensional vectors necessitates dimensionality reduction in order to reduce its storage and cpu costs to a reasonable range in spite of the popularity of various feature compression methods this paper shows that the feature dimension selection is a better choice for high dimensional fv vlad than the feature dimension compression methods e g product quantization we show that strong correlation among the feature dimensions in the fv and the vlad may not exist which renders feature selection a natural choice we also show that many dimensions in fv vlad are noise throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods to choose features we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases for visual recognition and image retrieval respectively combining with the 1 bit quantization feature selection has achieved both higher accuracy and less computational cost than feature compression methods such as product quantization on the fv and the vlad image representations

a finite difference scheme for three dimensional incompressible flows in cylindrical coordinates a finite difference scheme for the direct simulation of the incompressible time dependent three dimensional navier stokes equations in cylindrical coordinates is presented the equations in primitive variables vr v vzandp are solved by a fractional step method together with an approximate factorization technique cylindrical coordinates are singular at the axis the introduction of the radial fluxqr r vron a staggered grid simplifies the treatment of the region atr 0 the method is tested by comparing the evolution of a free vortex ring and its collision with a wall with the theory experiments and other numerical results the formation of a tripolar vortex where the highest vorticity is atr 0 is also considered finally to emphasize the accurate treatment near the axis the motion of a lamb dipole crossing the origin is simulated

a dual differentiable exact penalty function a new penalty function is associated with an inequality constrained nonlinear programming problem via its dual this penalty function is globally differentiable if the functions defining the original problem are twice globally differentiable in addition the penalty parameter remains finite this approach reduces the original problem to a simple problem of maximizing a globally differentiable function on the product space of a euclidean space and the nonnegative orthant of another euclidean space many efficient algorithms exist for solving this problem for the case of quadratic programming the penalty function problem can be solved effectively by successive overrelaxation sor methods which can handle huge problems while preserving sparsity features

a new exact penalty function for constrained smooth or nonsmooth optimization problems new continuously differentiable penalty functions are derived they are proved exact in the sense that under some nondegeneracy assumption local optimizers of a nonlinear program are precisely the optimizers of the associated penalty function this is achieved by augmenting the dimension of the program by a variable that controls both the weight of the penalty terms and the regularization of the nonsmooth terms

high speed tracking with kernelized correlation filters the core component of most modern trackers is a discriminative classifier tasked with distinguishing between the target and the surrounding environment to cope with natural image changes this classifier is typically trained with translated and scaled sample patches such sets of samples are riddled with redundancies any overlapping pixels are constrained to be the same based on this simple observation we propose an analytic model for datasets of thousands of translated patches by showing that the resulting data matrix is circulant we can diagonalize it with the discrete fourier transform reducing both storage and computation by several orders of magnitude interestingly for linear regression our formulation is equivalent to a correlation filter used by some of the fastest competitive trackers for kernel regression however we derive a new kernelized correlation filter kcf that unlike other kernel algorithms has the exact same complexity as its linear counterpart building on it we also propose a fast multi channel extension of linear correlation filters via a linear kernel which we call dual correlation filter dcf both kcf and dcf outperform top ranking trackers such as struck or tld on a 50 videos benchmark despite running at hundreds of frames per second and being implemented in a few lines of code algorithm 1 to encourage further developments our tracking framework was made open source

rotative maximal pattern inspired by the photometric invariance of color space this paper proposes a simple yet powerful descriptor for object detection and recognition called rotative maximal pattern rmp the effectiveness of rmp comes from the two components rotatable couple templates rcts with max pooling and normalized histogram intersection nhi with the theoretical guarantee more concretely rcts are the combination of two templates to code the possible rotations nhi serves as the similarity between two color histograms we have conducted extensive experiments on inria pedestrian and pascal voc2007 data sets for object detection tasks we also show that our approach leads to a promising performance on caltech 101 scene 15 uiucsport and stanford 40 action data sets

collaborative linear coding for robust image classification how to generate robust image representations when there is contamination from noisy pixels within the images is critical for boosting the performance of image classification methods however such an important problem is not fully explored yet in this paper we propose a novel image representation learning method i e collaborative linear coding clc to alleviate the negative influence of noisy features in classifying images specifically clc exploits the correlation among local features in the coding procedure in order to suppress the interference of noisy features via weakening their responses on coding basis clc implicitly divides the extracted local features into different feature subsets and such feature allocation is indicated by the introduced latent variables within each subset the features are ensured to be highly correlated and the produced codes for them are encouraged to activate on the identical basis through incorporating such regularization in the coding model the responses of noisy local features are dominated by the responses of informative features due to their rarity compared with the informative features thus the final image representation is more robust and distinctive for following classification compared with the coding methods without considering such high order correlation though clc involves a set of complicated optimization problems we investigate the special structure of the problems and then propose an efficient alternative optimization algorithm we verified the effectiveness and robustness of the proposed clc on multiple image classification benchmark datasets including scene 15 indoor 67 flower 102 pet 37 and pascal voc 2011 compared with the well established baseline llc clc consistently enhances the classification accuracy especially for the images containing more noises

existence of exact penalty for constrained optimization problems in hilbert spaces in this paper we use the penalty approach in order to study constrained minimization problems in a complete metric space with locally lipschitzian mixed constraints a penalty function is said to have the exact penalty property if there is a penalty coefficient for which a solution of an unconstrained penalized problem is a solution of the corresponding constrained problem in this paper we establish sufficient conditions for the exact penalty property

kernel based transition probability toward similarity measure for semi supervised learning for improving the classification performance on the cheap it is necessary to exploit both labeled and unlabeled samples by applying semi supervised learning methods most of which are built upon the pair wise similarities between the samples while the similarities have so far been formulated in a heuristic manner such as by k nn we propose methods to construct similarities from the probabilistic viewpoint the kernel based formulation of a transition probability is first proposed via comparing kernel least squares to variational least squares in the probabilistic framework the formulation results in a simple quadratic programming which flexibly introduces the constraint to improve practical robustness and is efficiently computed by smo the kernel based transition probability is by nature favorably sparse even without applying k nn and induces the similarity measure of the same characteristics besides to cope with multiple types of kernel functions the multiple transition probabilities obtained correspondingly from the kernels can be probabilistically integrated with prior probabilities represented by linear weights we propose a computationally efficient method to optimize the weights in a discriminative manner the optimized weights contribute to a composite similarity measure straightforwardly as well as to integrate the multiple kernels themselves as multiple kernel learning does which consequently derives various types of multiple kernel based semi supervised classification methods in the experiments on semi supervised classification tasks the proposed methods demonstrate favorable performances compared to the other methods in terms of classification performances and computation time

training dcnn by combining max margin max correlation objectives and correntropy loss for multilabel image classification in this paper we build a multilabel image classifier using a general deep convolutional neural network dcnn we propose a novel objective function that consists of three parts i e max margin objective max correlation objective and correntropy loss the max margin objective explicitly enforces that the minimum score of positive labels must be larger than the maximum score of negative labels by a predefined margin which not only improves accuracies of the multilabel classifier but also eases the threshold determination the max correlation objective can make the dcnn model learn a latent semantic space which maximizes the correlations between the feature vectors of the training samples and their corresponding ground truth label vectors projected into this space instead of using the traditional softmax loss we adopt the correntropy loss from the information theory field to minimize the training errors of the dcnn model the proposed framework can be end to end trained comprehensive experimental evaluations on pascal voc 2007 and mir flickr 25k multilabel benchmark data sets with four dcnn models i e alexnet vgg 16 googlenet and resnet demonstrate that the proposed objective function can remarkably improve the performance accuracies of a dcnn model for the task of multilabel image classification

differential earth mover s distance with its applications to visual tracking the earth mover s distance emd is a similarity measure that captures perceptual difference between two distributions its computational complexity however prevents a direct use in many applications this paper proposes a novel differential emd demd algorithm based on the sensitivity analysis of the simplex method and offers a speedup at orders of magnitude compared with its brute force counterparts the demd algorithm is discussed and empirically verified in the visual tracking context the deformations of the distributions for objects at different time instances are accommodated well by the emd and the differential algorithm makes the use of emd in real time tracking possible to further reduce the computation signatures i e variable size descriptions of distributions are employed as an object representation the new algorithm models and estimates local background scenes as well as foreground objects to handle scale changes in a principled way extensive quantitative evaluation of the proposed algorithm has been carried out using benchmark sequences and the improvement over the standard mean shift tracker is demonstrated

visual tracking via discriminative sparse similarity map in this paper we cast the tracking problem as finding the candidate that scores highest in the evaluation model based upon a matrix called discriminative sparse similarity map dss map this map demonstrates the relationship between all the candidates and the templates and it is constructed based on the solution to an innovative optimization formulation named multitask reverse sparse representation formulation which searches multiple subsets from the whole candidate set to simultaneously reconstruct multiple templates with minimum error a customized apg method is derived for getting the optimum solution in matrix form within several iterations this formulation allows the candidates to be evaluated accurately in parallel rather than one by one like most sparsity based trackers do and meanwhile considers the relationship between candidates therefore it is more superior in terms of cost performance ratio the discriminative information containing in this map comes from a large template set with multiple positive target templates and hundreds of negative templates a laplacian term is introduced to keep the coefficients similarity level in accordance with the candidates similarities thereby making our tracker more robust a pooling approach is proposed to extract the discriminative information in the dss map for easily yet effectively selecting good candidates from bad ones and finally get the optimum tracking results plenty experimental evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against the state of the art methods

product code vector quantizers for waveform and voice coding memory and computation requirements imply fundamental limitations on the quality that can be achieved in vector quantization systems used for speech waveform coding and linear predictive voice coding lpc one approach to reducing storage and computation requirements is to organize the set of reproduction vectors as the cartesian product of a vector codebook describing the shape of each reproduction vector and a scalar codebook describing the gain or energy such shape gain vector quantizers can be applied both to waveform coding using a quadratic error distortion measure and to voice coding using an itakura saito distortion measure in each case the minimum distortion reproduction vector can be found by first selecting a shape code word and then based on that choice selecting a gain codeword several algorithms are presented for the design of shape gain vector quantizers based on a traning sequence of data or a probabilistic model the algorithms are used to design shape gain vector quantizers for both the waveform coding and voice coding application the quantizers are simulated and their performance is compared to that of previously reported vector quantization systems

penalty for zero one integer equivalent problem the equivalence of zero one integer programming and a concave quadratic penalty function problem has been shown by raghavachari for a sufficiently large value of the penalty a lower bound for this penalty is obtained here which in specific cases cannot be reduced

combined immersed boundary finite difference methods for three dimensional complex flow simulations a second order accurate highly efficient method is developed for simulating unsteady three dimensional incompressible flows in complex geometries this is achieved by using boundary body forces that allow the imposition of the boundary conditions on a given surface not coinciding with the computational grid the governing equations therefore can be discretized and solved on a regular mesh thus retaining the advantages and the efficiency of the standard solution procedures two different forcings are tested showing that while the quality of the results is essentially the same in both cases the efficiency of the calculation strongly depends on the particular expression a major issue is the interpolation of the forcing over the grid that determines the accuracy of the scheme this ranges from zeroth order for the most commonly used interpolations up to second order for an ad hoc velocity interpolation the present scheme has been used to simulate several flows whose results have been validated by experiments and other results available in the literature finally in the last example we show the flow inside an ic piston cylinder assembly at high reynolds number to our knowledge this is the first example in which the immersed boundary technique is applied to a full three dimensional complex flow with moving boundaries and with a reynolds number high enough to require a subgrid scale turbulence model

texture classification using dense micro block difference this paper is devoted to the problem of texture classification motivated by recent advancements in the field of compressive sensing and keypoints descriptors a set of novel features called dense micro block difference dmd is proposed these features provide highly descriptive representation of image patches by densely capturing the granularities at multiple scales and orientations unlike most of the earlier work on local features the dmd does not involve any quantization thus retaining the complete information we demonstrate that the dmd have dimensionality much lower than scale invariant feature transform sift and can be computed using integral image much faster than sift the proposed features are encoded using the fisher vector method to obtain an image descriptor which considers high order statistics the proposed image representation is combined with the linear support vector machine classifier extensive experiments are conducted on five texture data sets kth tips umd kth tips 2a brodatz and curet using standard protocols the results demonstrate that our approach outperforms the state of the art in texture classification

a novel locally linear knn method with applications to visual recognition a locally linear k nearest neighbor llk method is presented in this paper with applications to robust visual recognition specifically the concept of an ideal representation is first presented which improves upon the traditional sparse representation in many ways the objective function based on a host of criteria for sparsity locality and reconstruction is then optimized to derive a novel representation which is an approximation to the ideal representation the novel representation is further processed by two classifiers namely an llk based classifier and a locally linear nearest mean based classifier for visual recognition the proposed classifiers are shown to connect to the bayes decision rule for minimum error additional new theoretical analysis is presented such as the nonnegative constraint the group regularization and the computational efficiency of the proposed llk method new methods such as a shifted power transformation for improving reliability a coefficients truncating method for enhancing generalization and an improved marginal fisher analysis method for feature extraction are proposed to further improve visual recognition performance extensive experiments are implemented to evaluate the proposed llk method for robust visual recognition in particular eight representative data sets are applied for assessing the performance of the llk method for various visual recognition applications such as action recognition scene recognition object recognition and face recognition

robustifying descriptor instability using fisher vectors many computer vision applications including image classification matching and retrieval use global image representations such as the fisher vector to encode a set of local image patches to describe these patches many local descriptors have been designed to be robust against lighting changes and noise however local image descriptors are unstable when the underlying image signal is low such low signal patches are sensitive to small image perturbations which might come e g from camera noise or lighting effects in this paper we first quantify the relation between the signal strength of a patch and the instability of that patch and second we extend the standard fisher vector framework to explicitly take the descriptor instabilities into account in comparison to common approaches to dealing with descriptor instabilities our results show that modeling local descriptor instability is beneficial for object matching image retrieval and classification

mofap a multi level representation for action recognition this paper proposes a multi level video representation by stacking the activations of motion features atoms and phrases mofap motion features refer to those low level local descriptors while motion atoms and phrases can be viewed as mid level temporal parts motion atom is defined as an atomic part of action and captures the motion information of video in a short temporal scale motion phrase is a temporal composite of multiple motion atoms defined with an and or structure it further enhances the discriminative capacity of motion atoms by incorporating temporal structure in a longer temporal scale specifically we first design a discriminative clustering method to automatically discover a set of representative motion atoms then we mine effective motion phrases with high discriminative and representative capacity in a bottom up manner based on these basic units of motion features atoms and phrases we construct a mofap network by stacking them layer by layer this mofap network enables us to extract the effective representation of video data from different levels and scales the separate representations from motion features motion atoms and motion phrases are concatenated as a whole one called activation of mofap the effectiveness of this representation is demonstrated on four challenging datasets olympic sports ucf50 hmdb51 and ucf101 experimental results show that our representation achieves the state of the art performance on these datasets

local phase quantization plus a principled method for embedding local phase quantization into fisher vector for blurred image recognition abstract advances in computer vision and image processing technology have led to great success in image recognition when the images are clear however in real world applications images are often blurred due to factors such as atmospheric turbulence object camera relative motion and focus this imposes a great challenge on practical image recognition tasks to improve the performance of burred image recognition one main approach is to extract blur insensitive visual descriptors a well established blur insensitive texture feature local phase quantization lpq can achieve promising results with a trade off between effectiveness and efficiency however for complicated visual recognition tasks its performance is still not satisfactory to leverage the discriminative power of lpq we propose local phase quantization plus lpq which embeds lpq into fisher vector fv to acquire mid level blurred image representation under the bag of words bow model to better fit fv instead of using real and imaginary parts as in lpq lpq directly quantizes the local phases of the short term fourier transform stft directly this results in lower dimensionality features but stronger local pattern characterization power lpq is densely sampled for blurred image representation a sliding window screens the image with vertical and horizontal strides lpq s are then acquired from all resulting local regions to better maintain spatial structure characteristics the sliding window is divided into finer cells after being fv encoded local lpq s are aggregated through sum pooling to generate the image signature a wide range of experiments on 5 challenging datasets of different types textures faces scenes clouds and flower demonstrate that lpq significantly outperforms lpq and other well established visual features in discriminative power