randomized clustering forests for image classification some of the most effective recent methods for content based image classification work by quantizing image descriptors and accumulating histograms of the resulting visual word codes large numbers of descriptors and large codebooks are required for good results and this becomes slow using k means we introduce extremely randomized clustering forests ensembles of randomly created clustering trees and show that they provide more accurate results much faster training and testing and good resistance to background clutter second an efficient image classification method is proposed it combines erc forests and saliency maps very closely with the extraction of image information for a given image a classifier builds a saliency map online and uses it to classify the image we show in several state of the art image classification tasks that this method can speed up the classification process enormously finally we show that the proposed erc forests can also be used very successfully for learning distance between images the distance computation algorithm consists of learning the characteristic differences between local descriptors sampled from pairs of same or different objects these differences are vector quantized by erc forests and the similarity measure is computed from this quantization the similarity measure has been evaluated on four very different datasets and always outperforms the state of the art competitive approaches

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

action recognition with dynamic image networks we introduce the concept of dynamic image a novel compact representation of videos useful for video analysis particularly in combination with convolutional neural networks cnns a dynamic image encodes temporal data such as rgb or optical flow videos by using the concept of rank pooling the idea is to learn a ranking machine that captures the temporal evolution of the data and to use the parameters of the latter as a representation we call the resulting representation dynamic image because it summarizes the video dynamics in addition to appearance this powerful idea allows to convert any video to an image so that existing cnn models pre trained with still images can be immediately extended to videos we also present an efficient approximate rank pooling operator that runs two orders of magnitude faster than the standard ones with any loss in ranking performance and can be formulated as a cnn layer to demonstrate the power of the representation we introduce a novel four stream cnn architecture which can learn from rgb and optical flow frames as well as from their dynamic image representations we show that the proposed network achieves state of the art performance 95 5 and 72 5 percent accuracy in the ucf101 and hmdb51 respectively

kernelized multiview projection for robust action recognition conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features in this paper we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called kernelized multiview projection kmp computing the kernel matrices from different features views via time sequential distance learning kmp can encode different features with different weights to achieve a low dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative more crucially kmp is linear for the reproducing kernel hilbert space which allows it to be competent for various practical applications we demonstrate kmp s performance for action recognition on five popular action datasets and the results are consistently superior to state of the art techniques

proper orthogonal decomposition for parameter estimation in oscillating biological networks proper orthogonal decomposition pod is frequently applied to estimate parameters of partial differential equations this study examines the application of the pod method in estimating the parameters of an ordinary differential equation ode model of stable oscillating biological networks the mathematical model used to simulate molecular interactions in these oscillating networks is related to the gause lotka volterra equations the findings reveal that pod generates accurate estimates of the parameters even in the presence of experimental noise furthermore extrapolating biologically measured data points to a number of oscillations improves the curve fits c 1 approximations and parameter estimations

action recognition based on overcomplete independent components analysis existing works on action recognition rely on two separate stages 1 designing hand designed features or learning features from video data 2 classifying features using a classifier such as svm or adaboost motivated by two observations 1 independent component analysis ica is capable of encoding intrinsic features underlying video data and 2 videos of different actions can be easily distinguished by their intrinsic features we propose a simple but effective action recognition framework based on the recently proposed overcomplete ica model after a set of overcomplete ica basis functions are learned from the densely sampled 3d patches from training videos for each action a test video is classified as the class whose basis functions can reconstruct the sampled 3d patches from the test video with the smallest reconstruction error the experimental results on five benchmark datasets demonstrate that the proposed approach outperforms several state of the art works

robust optical flow integration we analyze the problem of how to correctly construct dense point trajectories from optical flow fields first we show that simple euler integration is unavoidably inaccurate no matter how good is the optical flow estimator then an inverse integration scheme is analyzed which is more robust to bias and input noise and shows better stability properties our contribution is threefold 1 a theoretical analysis that demonstrates why and in what sense inverse integration is more accurate 2 a rich experimental validation both on synthetic and real image data and 3 an algorithm for approximate online inverse integration this new technique is precious whether one is trying to propagate information densely available on a reference frame to the other frames in the sequence or conversely to assign information densely over each frame by pulling it from the reference

mathematical and numerical results on the sensitivity of the pod approximation relative to the burgers equation from mathematical and numerical points of view we study the sensitivity with respect to parametric evolutions of the error obtained by approximating a given parametric partial differential equation using a proper orthogonal decomposition pod basis determined once and for all with association to a fixed parameter more precisely we will be considering the one dimensional burgers equation with a source term

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

highly articulated kinematic structure estimation combining motion and skeleton information in this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single view 2d image sequence in contrast to prior motion based methods which estimate relatively simple articulations our method can generate arbitrarily complex kinematic structures with skeletal topology via a successive iterative merging strategy the iterative merge process is guided by a density weighted skeleton map which is generated from a novel object boundary generation method from sparse 2d feature points our main contributions can be summarised as follows i an unsupervised complex articulated kinematic structure estimation method that combines motion segments with skeleton information ii an iterative fine to coarse merging strategy for adaptive motion segmentation and structural topology embedding iii a skeleton estimation method based on a novel silhouette boundary generation from sparse feature points using an adaptive model selection method iv a new highly articulated object dataset with ground truth annotation we have verified the effectiveness of our proposed method in terms of computational time and estimation accuracy through rigorous experiments with multiple datasets our experiments show that the proposed method outperforms state of the art methods both quantitatively and qualitatively

a discriminative representation for human action recognition action recognition has been standing as an active research topic over the past years many efforts have been made and many methods have been proposed however there are still some challenges such as illumination condition viewpoint camera motion and cluttered background in order to tackle these challenges a discriminative representation is proposed by discovering key information of the input data this task can be addressed by improvements of two major components parameterized representation and discriminative classifier the representation is parameterized with hidden variables and can be learned from training data and the classifier can be trained to recognize actions based on the proposed representation the contributions of this paper are as follows 1 a novel probabilistic representation is utilized to capture the relative significant information of low level features 2 a novel framework is proposed by combining the parameterized representation and discriminative classifier 3 an alternating strategy is favorable to improve the performance of action recognition by updating the representation and the classifier alternatively experimental results on five well known datasets demonstrate that the proposed method significantly improves the performance in action recognition highlightsa discriminative representation is proposed by discovering key information of the input data the representation is parameterized with hidden variables and can be learned from training data human action is recognized by combining the parameterized representation and discriminative classifier the performance of action recognition is improved by updating the representation and the classifier alternatively

least squares quantization in pcm it has long been realized that in pulse code modulation pcm with a given ensemble of signals to handle the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall it has been shown by panter and dite that in the limit as the number of quanta becomes infinite the asymptotic fractional density of quanta per unit voltage should vary as the one third power of the probability density per unit voltage of signal amplitudes in this paper the corresponding result for any finite number of quanta is derived that is necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy the optimization criterion used is that the average quantization noise power be a minimum it is shown that the result obtained here goes over into the panter and dite result as the number of quanta become large the optimum quautization schemes for 2 b quanta b 1 2 cdots 7 are given numerically for gaussian and for laplacian distribution of signal amplitudes

global hashing system for fast image search hashing methods have been widely investigated for fast approximate nearest neighbor searching in large data sets most existing methods use binary vectors in lower dimensional spaces to represent data points that are usually real vectors of higher dimensionality we divide the hashing process into two steps data points are first embedded in a low dimensional space and the global positioning system method is subsequently introduced but modified for binary embedding we devise dataindependent and data dependent methods to distribute the satellites at appropriate locations our methods are based on finding the tradeoff between the information losses in these two steps experiments show that our data dependent method outperforms other methods in different sized data sets from 100k to 10m by incorporating the orthogonality of the code matrix both our data independent and data dependent methods are particularly impressive in experiments on longer bits

a reduced order extrapolated finite difference iterative scheme based on pod method for 2d sobolev equation abstract in this study we devote ourselves to the reduced order extrapolated finite difference iterative roefdi modeling and analysis for the two dimensional 2d sobolev equation to this end we first establish the reduced order extrapolated finite difference iterative roefdi scheme holding sufficiently high accuracy but containing very few degrees of freedom for the 2d sobolev equation via the proper orthogonal decomposition pod technique and then we analyze the stability and convergence of the roefdi solutions finally we use the numerical experiments to verify the feasibility and effectiveness of the roefdi scheme

a parameterized non intrusive reduced order model and error analysis for general time dependent nonlinear partial differential equations and its applications abstract a novel parameterized non intrusive reduced order model p nirom based on proper orthogonal decomposition pod has been developed this p nirom is a generic and efficient approach for model reduction of parameterized partial differential equations p pdes over existing parameterized reduced order models p rom most of them are based on the reduced basis method it is non intrusive and independent on partial differential equations and computational codes during the training process the smolyak sparse grid method is used to select a set of parameters over a specific parameterized space p r p for each selected parameter the reduced basis functions are generated from the snapshots derived from a run of the high fidelity model more generally the snapshots and basis function sets for any parameters over p can be obtained using an interpolation method the p nirom can then be constructed by using our recently developed technique xiao et al 2015 41 42 where either the smolyak or radial basis function rbf methods are used to generate a set of hyper surfaces representing the underlying dynamical system over the reduced space the new p nirom technique has been applied to parameterized navier stokes equations and implemented with an unstructured mesh finite element model the capability of this p nirom has been illustrated numerically by two test cases flow past a cylinder and lock exchange case the prediction capabilities of the p nirom have been evaluated by varying the viscosity initial and boundary conditions the results show that this p nirom has captured the quasi totality of the details of the flow with cpu speedup of three orders of magnitude an error analysis for the p nirom has been carried out

learning sample specific weights for late fusion late fusion is one of the most effective approaches to enhance recognition accuracy through combining prediction scores of multiple classifiers each of which is trained by a specific feature or model the existing methods generally use a fixed fusion weight for one classifier over all samples and ignore the fact that each classifier may perform better or worse for different subsets of samples in order to address this issue we propose a novel sample specific late fusion sslf method specifically we cast late fusion into an information propagation process that diffuses the fusion weights of labeled samples to the individual unlabeled samples and enforce positive samples to have higher fusion scores than negative samples upon this process the optimal fusion weight for each sample is identified while positive samples are pushed toward the top at the fusion score rank list to achieve better accuracy in this paper two sslf methods are presented the first method is ranking sslf r sslf which is based on graph laplacian with ranksvm style constraints we formulate and solve the problem with a fast gradient projection algorithm the second method is infinite push sslf i sslf which combines graph laplacian with infinite push constraints i sslf is a l infty norm constrained optimization problem and can be solved by an efficient alternating direction method of multipliers method extensive experiments on both large scale image and video data sets demonstrate the effectiveness of our methods in addition in order to make our method scalable to support large data sets the anchorgraph model is employed to propagate information on a subset of samples anchor points and then reconstruct the entire graph to get the weights of all samples to the best of our knowledge this is the first method that supports learning of sample specific fusion weights for late fusion

mixed finite element formulation and error estimates based on proper orthogonal decomposition for the nonstationary navier stokes equations in this paper proper orthogonal decomposition pod is used for model reduction of mixed finite element mfe for the nonstationary navier stokes equations and error estimates between a reference solution and the pod solution of reduced mfe formulation are derived the basic idea of this reduction technique is that ensembles of data are first compiled from transient solutions computed equation system derived with the usual mfe method for the nonstationary navier stokes equations or from physics system trajectories by drawing samples of experiments and interpolation or data assimilation and then the basis functions of the usual mfe method are substituted with the pod basis functions reconstructed by the elements of the ensemble to derive the pod reduced mfe formulation for the nonstationary navier stokes equations it is shown by considering numerical simulation results obtained for the illustrating example of cavity flows that the error between pod solution of reduced mfe formulation and the reference solution is consistent with theoretical results moreover it is also shown that this result validates the feasibility and efficiency of the pod method

variational method for joint optical flow estimation and edge aware image restoration the most popular optical flow algorithms rely on optimizing the energy function that integrates a data term and a smoothness term in contrast to this traditional framework we derive a new objective function that couples optical flow estimation and image restoration our method is inspired by the recent successes of edge aware constraints eac in preserving edges in general gradient domain image filtering by incorporating an eac image fidelity term ift in the conventional variational model the new energy function can simultaneously estimate optical flow and restore images with preserved edges in a bidirectional manner for the energy minimization we rewrite the eac into gradient form and optimize the ift with euler lagrange equations we can thus apply the image restoration by analytically solving a system of linear equations our eac combined ift is easy to implement and can be seamlessly integrated into various optical flow functions suggested in literature extensive experiments on public optical flow benchmarks demonstrate that our method outperforms the current state of the art in optical flow estimation and image restoration highlightsincorporating an eac added ift to the variational model to form a new energy function which can estimate optical flow and restore images jointly the eac can be rewritten into the first order gradient form and is beneficial for preserving edges and minimization input images can be fast restored by optimizing the euler lagrange equations of the eac integrated ift

reduced order modeling of the upper tropical pacific ocean model using proper orthogonal decomposition the proper orthogonal decomposition pod is shown to be an efficient model reduction technique for simulating physical processes governed by partial differential equations in this paper we make an initial effort to investigate problems related to pod reduced modeling of a large scale upper ocean circulation in the tropic pacific domain we construct different pod models with different choices of snapshots and different number of pod basis functions the results from these different pod models are compared with that of the original model the main findings are 1 the large scale seasonal variability of the tropic pacific obtained by the original model is well captured by a low dimensional system of order 22 which is constructed using 20 snapshots and 7 leading pod basis functions 2 the rms errors for the upper ocean layer thickness of the pod model of order 22 are less than 1m that is less than 1 of the average thickness and the correlation between the upper ocean layer thickness with that from the pod model is around 0 99 3 retaining modes that capture 99 energy is necessary in order to construct pod models yielding a high accuracy

impulsive fractional partial differential equations this paper deals with cauchy problem for a class of impulsive partial hyperbolic differential equations involving the caputo derivative our first purpose is to show that the formula of solutions in cited papers are incorrect next we reconsider a class of impulsive fractional partial hyperbolic differential equations and introduce a correct formula of solutions for cauchy problem in r n further some sufficient conditions for existence of the solutions are established by applying fixed point method at last we consider the cauchy problem in a banach space via the technique of measures of noncompactness and monch s fixed point theorem some examples are given to illustrate our results

domain generalization and adaptation using low rank exemplar svms domain adaptation between diverse source and target domains is challenging especially in the real world visual recognition tasks where the images and videos consist of significant variations in viewpoints illuminations qualities etc in this paper we propose a new approach for domain generalization and domain adaptation based on exemplar svms specifically we decompose the source domain into many subdomains each of which contains only one positive training sample and all negative samples each subdomain is relatively less diverse and is expected to have a simpler distribution by training one exemplar svm for each subdomain we obtain a set of exemplar svms to further exploit the inherent structure of source domain we introduce a nuclear norm based regularizer into the objective function in order to enforce the exemplar svms to produce a low rank output on training samples in the prediction process the confident exemplar svm classifiers are selected and reweigted according to the distribution mismatch between each subdomain and the test sample in the target domain we formulate our approach based on the logistic regression and least square svm algorithms which are referred to as low rank exemplar svms lre svms and low rank exemplar least square svms lre lssvms respectively a fast algorithm is also developed for accelerating the training of lre lssvms we further extend domain adaptation machine dam to learn an optimal target classifier for domain adaptation and show that our approach can also be applied to domain adaptation with evolving target domain where the target data distribution is gradually changing the comprehensive experiments for object recognition and action recognition demonstrate the effectiveness of our approach for domain generalization and domain adaptation with fixed and evolving target domains

discriminative video pattern search for efficient action detection actions are spatiotemporal patterns similar to the sliding window based object detection action detection finds the reoccurrences of such spatiotemporal patterns through pattern matching by handling cluttered and dynamic backgrounds and other types of action variations we address two critical issues in pattern matching based action detection 1 the intrapattern variations in actions and 2 the computational efficiency in performing action pattern search in cluttered scenes first we propose a discriminative pattern matching criterion for action classification called naive bayes mutual information maximization nbmim each action is characterized by a collection of spatiotemporal invariant features and we match it with an action class by measuring the mutual information between them based on this matching criterion action detection is to localize a subvolume in the volumetric video space that has the maximum mutual information toward a specific action class a novel spatiotemporal branch and bound stbb search algorithm is designed to efficiently find the optimal solution our proposed action detection method does not rely on the results of human detection tracking or background subtraction it can handle action variations such as performing speed and style variations as well as scale changes well it is also insensitive to dynamic and cluttered backgrounds and even to partial occlusions the cross data set experiments on action detection including kth cmu action data sets and another new msr action data set demonstrate the effectiveness and efficiency of the proposed multiclass multiple instance action detection method

fusing mathcal r r features and local features with context aware kernels for action recognition the performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations in this paper we combine two kinds of features extracted from the spatio temporal interest points with context aware kernels for action recognition for the action representation local cuboid features extracted around interest points are very popular using a bag of visual words bovw model such representations however ignore potentially valuable information about the global spatio temporal distribution of interest points we propose a new global feature to capture the detailed geometrical distribution of interest points it is calculated by using the 3d mathcal r r transform which is defined as an extended 3d discrete radon transform followed by the application of a two directional two dimensional principal component analysis for the similarity measurement we model a video set as an optimized probabilistic hypergraph and propose a context aware kernel to measure high order relationships among videos the context aware kernel is more robust to the noise and outliers in the data than the traditional context free kernel which just considers the pairwise relationships between videos the hyperedges of the hypergraph are constructed based on a learnt mahalanobis distance metric any disturbing information from other classes is excluded from each hyperedge finally a multiple kernel learning algorithm is designed by integrating the l 2 l2 norm regularization into a linear svm classifier to fuse the mathcal r r feature and the bovw representation for action recognition experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition

the ordinal relation preserving binary codes hashing algorithm has been widely used for efficient approximate nearest neighbor ann search learning optimal hashing functions has been given focus and it is still a challenge this paper aims to effectively and efficiently generate relative similarity preserving binary codes most existing hashing methods try to preserve the locality similarity by preserving direct distance similarity while ignoring the relative similarity which advantages in ann search in this paper this issue is solved by proposing the relative error which emphasizes that the ordinal relations in hamming space and euclidean space should be consistent with each other we learn hashing projection functions via two steps the first step adopts the lookup based mechanism to find the optimal binary codes of training data which can preserve the relative similarity and simultaneously adapt to data distribution the binary codes in the first step are considered as supervision information in the second step the objective of the second step is to learn hashing projection functions which can efficiently regenerate the binary codes in the first step aim to be in accordance with the property of data distribution the hyper internal tangent planes of two specified spheres are chosen as hashing projection functions assisted by these projection functions the time complexity of encoding process is greatly reduced experimental results on four public data sets demonstrate that our method outperforms many other state of the art methods author highlightsan effective and efficient binary coding framework which enjoys the merits of lookup based and hamming based methods is proposed relative error is proposed to preserve the relative sensitivity the effective tangent planes are adopted as hashing functions to avoid computing the complex classification problem the encoding error guarantees that the selected hashing functions have good performance

a reduced order extrapolation central difference scheme based on pod for two dimensional fourth order hyperbolic equations the reduced order extrapolating fd schemes are just developed by luo s research team since 2013 see 47 52 and a very new technique the reduced order extrapolation central difference scheme based on pod for two dimensional fourth order hyperbolic equations is first built by us we provide theoretical analysis and examples to show the advantage of our method this paper is concerned with establishing the reduced order extrapolation central difference roecd scheme based on proper orthogonal decomposition pod for two dimensional 2d fourth order hyperbolic equations for this purpose we first develop the classical central difference cd scheme for the 2d fourth order hyperbolic equations and analyze its stability and convergence then by making use of the pod method we build the roecd scheme with fewer degrees of freedom and sufficiently high accuracy and furnish the error estimates of the roecd solutions and the algorithm procedure for solving the roecd scheme finally we employ some numerical examples to confirm the correctness of theoretical conclusions this implies that roecd scheme is feasible and efficient for seeking the numerical solutions of the 2d fourth order hyperbolic equations

activity representation with motion hierarchies complex activities e g pole vaulting are composed of a variable number of sub events connected by complex spatio temporal relations whereas simple actions can be represented as sequences of short temporal parts in this paper we learn hierarchical representations of activity videos in an unsupervised manner these hierarchies of mid level motion components are data driven decompositions specific to each video we introduce a spectral divisive clustering algorithm to efficiently extract a hierarchy over a large number of tracklets i e local trajectories we use this structure to represent a video as an unordered binary tree we model this tree using nested histograms of local motion features we provide an efficient positive definite kernel that computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent child relations we present experimental results on four recent challenging benchmarks the high five dataset patron perez et al high five recognising human interactions in tv shows 2010 the olympics sports dataset niebles et al modeling temporal structure of decomposable motion segments for activity classification 2010 the hollywood 2 dataset marszalek et al actions in context 2009 and the hmdb dataset kuehne et al hmdb a large video database for human motion recognition 2011 we show that per video hierarchies provide additional information for activity recognition our approach improves over unstructured activity models baselines using other motion decomposition algorithms and the state of the art

dense trajectories and motion boundary descriptors for action recognition this paper introduces a video representation based on dense trajectories and motion boundary descriptors trajectories capture the local motion information of the video a dense representation guarantees a good coverage of foreground motion as well as of the surrounding context a state of the art optical flow algorithm enables a robust and efficient extraction of dense trajectories as descriptors we extract features aligned with the trajectories to characterize shape point coordinates appearance histograms of oriented gradients and motion histograms of optical flow additionally we introduce a descriptor based on motion boundary histograms mbh which rely on differential optical flow the mbh descriptor shows to consistently outperform other state of the art descriptors in particular on real world videos that contain a significant amount of camera motion we evaluate our video representation in the context of action classification on nine datasets namely kth youtube hollywood2 ucf sports ixmas uiuc olympic sports ucf50 and hmdb51 on all datasets our approach outperforms current state of the art results

3d shape matching via two layer coding view based 3d shape retrieval is a popular branch in 3d shape analysis owing to the high discriminative property of 2d views however many previous works do not scale up to large 3d shape databases we propose a two layer coding tlc framework to conduct shape matching much more efficiently the first layer coding is applied to pairs of views represented as depth images the spatial relationship of each view pair is captured with so called eigen angle which is the planar angle between the two views measured at the center of the 3d shape prior to the second layer coding the view pairs are divided into subsets according to their eigen angles consequently view pairs that differ significantly in their eigen angles are encoded with different codewords which implies that spatial arrangement of views is preserved in the second layer coding the final feature vector of a 3d shape is the concatenation of all the encoded features from different subsets which is used for efficient indexing directly tlc is not limited to encode the local features from 2d views but can be also applied to encoding 3d features exhaustive experimental results confirm that tlc achieves state of the art performance in both retrieval accuracy and efficiency

a reduced finite difference scheme based on singular value decomposition and proper orthogonal decomposition for burgers equation in this article a reduced optimizing finite difference scheme fds based on singular value decomposition svd and proper orthogonal decomposition pod for burgers equation is presented also the error estimates between the usual finite difference solution and the pod solution of reduced optimizing fds are analyzed it is shown by considering the results obtained for numerical simulations of cavity flows that the error between the pod solution of reduced optimizing fds and the solution of the usual fds is consistent with theoretical results moreover it is also shown that the reduced optimizing fds is feasible and efficient

scalable similarity search with topology preserving hashing hashing based similarity search techniques is becoming increasingly popular in large data sets to capture meaningful neighbors the topology of a data set which represents the neighborhood relationships between its subregions and the relative proximities between the neighbors of each subregion e g the relative neighborhood ranking of each subregion should be exploited however most existing hashing methods are developed to preserve neighborhood relationships while ignoring the relative neighborhood proximities moreover most hashing methods lack in providing a good result ranking since there are often lots of results sharing the same hamming distance to a query in this paper we propose a novel hashing method to solve these two issues jointly the proposed method is referred to as topology preserving hashing tph tph is distinct from prior works by also preserving the neighborhood ranking based on this framework we present three different tph methods including linear unsupervised tph semisupervised tph and kernelized tph particularly our unsupervised tph is capable of mining semantic relationship between unlabeled data without supervised information extensive experiments on four large data sets demonstrate the superior performances of the proposed methods over several state of the art unsupervised and semisupervised hashing techniques

two level discretizations of nonlinear closure models for proper orthogonal decomposition proper orthogonal decomposition has been successfully used in the reduced order modeling of complex systems its original promise of computationally efficient yet accurate approximation of coherent structures in high reynolds number turbulent flows however still remains to be fulfilled to balance the low computational cost required by reduced order modeling and the complexity of the targeted flows appropriate closure modeling strategies need to be employed since modern closure models for turbulent flows are generally nonlinear their efficient numerical discretization within a proper orthogonal decomposition framework is challenging this paper proposes a two level method for an efficient and accurate numerical discretization of general nonlinear closure models for proper orthogonal decomposition reduced order models the two level method computes the nonlinear terms of the reduced order model on a coarse mesh compared with a brute force computational approach in which the nonlinear terms are evaluated on the fine mesh at each time step the two level method attains the same level of accuracy while dramatically reducing the computational cost we numerically illustrate these improvements in the two level method by using it in three settings the one dimensional burgers equation with a small diffusion parameter n 10 3 the two dimensional flow past a cylinder at reynolds number re 200 and the three dimensional flow past a cylinder at reynolds number re 1000

graph embedded one class classifiers for media data classification this paper introduces the graph embedded one class support vector machine and graph embedded support vector data description methods these methods constitute novel extensions of the one class support vectors machines and support vector data description incorporating generic graph structures that express geometric data relationships of interest in their optimization process local or global relationships between the training patterns can be expressed with single graphs or combinations of fully connected and knn graphs we show that the adoption of generic geometric class information acts as a regularizer to the solution of the original methods moreover we prove that the regularized solutions for both one class support vector machine and support vector data description are equivalent to applying the original methods in a transformed and shared feature space qualitative and quantitative evaluation of the proposed methods shows that they compare favorably to the standard oc svm and svdd classifiers respectively highlightsintroducing the graph embedded one class svm and graph embedded svdd methods exploiting data relationships in the optimization process adopting generic geometric class information acts as a regularizer the regularized solutions for both proposed methods are equivalent they compare favorably to the standard oc svm and svdd classifiers

large displacement optical flow descriptor matching in variational motion estimation optical flow estimation is classically marked by the requirement of dense sampling in time while coarse to fine warping schemes have somehow relaxed this constraint there is an inherent dependency between the scale of structures and the velocity that can be estimated this particularly renders the estimation of detailed human motion problematic as small body parts can move very fast in this paper we present a way to approach this problem by integrating rich descriptors into the variational optical flow setting this way we can estimate a dense optical flow field with almost the same high accuracy as known from variational optical flow while reaching out to new domains of motion analysis where the requirement of dense sampling in time is no longer satisfied

stabilized reduced order models for the advection diffusion reaction equation using operator splitting reduced order modeling rom coupled with finite element methods has been used effectively in many disciplines to efficiently solve complex problems however for advection dominated flows numerical simulations often contain spurious nonphysical oscillations which will also be apparent in the rom simulations in this work we consider stabilization methods for rom for the advection diffusion reaction adr equation when it is solved both with and without operator splitting specifically we consider the streamline upwind petrov galerkin supg stabilization method and the spurious oscillations at layers diminishing sold stabilization method we build on these methods by constructing a coherent framework which successfully integrates these model reduction stabilization and operator splitting approaches and we provide numerical examples detailing the application of this framework in the adr setting the stabilized rom results are compared numerically with their corresponding full finite element simulations

locality sensitive hashing for chi2 distance in the past 10 years new powerful algorithms based on efficient data structures have been proposed to solve the problem of nearest neighbors search or approximate nearest neighbors search if the euclidean locality sensitive hashing algorithm which provides approximate nearest neighbors in a euclidean space with sublinear complexity is probably the most popular the euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the earth mover distance and 2 distances in this paper we present a new lsh scheme adapted to 2 distance for approximate nearest neighbors search in high dimensional spaces we define the specific hashing functions we prove their local sensitivity and compare through experiments our method with the euclidean locality sensitive hashing algorithm in the context of image retrieval on real image databases the results prove the relevance of such a new lsh scheme either providing far better accuracy in the context of image retrieval than euclidean scheme for an equivalent speed or providing an equivalent accuracy but with a high gain in terms of processing speed

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

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

non linear petrov galerkin methods for reduced order modelling of the navier stokes equations using a mixed finite element pair abstract a new nonlinear petrov galerkin approach has been developed for proper orthogonal decomposition pod reduced order modelling rom of the navier stokes equations the new method is based on the use of the cosine rule between the advection direction in cartesian space time and the direction of the gradient of the solution a finite element pair p 1 dg p 2 which has good balance preserving properties is used here consisting of a mix of discontinuous for velocity components and continuous for pressure basis functions the contribution of the present paper lies in applying this new non linear petrov galerkin method to the reduced order navier stokes equations and thus improving the stability of rom results without tuning parameters the results of numerical tests are presented for a wind driven 2d gyre and the flow past a cylinder which are simulated using the unstructured mesh finite element cfd model in order to illustrate the numerical performance of the method the numerical results obtained show that the newly proposed pod petrov galerkin method can provide more accurate and stable results than the pod bubnov galerkin method

space time tree ensemble for action recognition and localization human actions are inherently structured patterns of body movements we explore ensembles of hierarchical spatio temporal trees discovered directly from training data to model these structures for action recognition and spatial localization discovery of frequent and discriminative tree structures is challenging due to the exponential search space particularly if one allows partial matching we address this by first building a concise action word vocabulary via discriminative clustering of the hierarchical space time segments which is a two level video representation that captures both static and non static relevant space time segments of the video using this vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of discriminative tree patterns our experiments show that these tree patterns alone or in combination with shorter patterns action words and pairwise patterns achieve promising performance on three challenging datasets ucf sports highfive and hollywood3d moreover we perform cross dataset validation using trees learned on highfive to recognize the same actions in hollywood3d and using trees learned on ucf sports to recognize and localize the similar actions in jhmdb the results demonstrate the potential for cross dataset generalization of the trees our approach discovers

proper orthogonal decomposition closure models for turbulent flows a numerical comparison this paper puts forth two new closure models for the proper orthogonal decomposition reduced order modeling of structurally dominated turbulent flows the dynamic subgrid scale model and the variational multiscale model these models which are considered state of the art in large eddy simulation together with the mixing length and the smagorinsky closure models are tested in the numerical simulation of 3d turbulent flow past a circular cylinder at re 1000 five criteria are used to judge the performance of the proper orthogonal decomposition reduced order models the kinetic energy spectrum the mean velocity the reynolds stresses the root mean square values of the velocity fluctuations and the time evolution of the pod coefficients all the numerical results are benchmarked against a direct numerical simulation based on these numerical results we conclude that the dynamic subgrid scale and the variational multiscale models are the most accurate

iterative quantization a procrustean approach to learning binary codes for large scale image retrieval this paper addresses the problem of learning similarity preserving binary codes for efficient similarity search in large scale image collections we formulate this problem in terms of finding a rotation of zero centered data so as to minimize the quantization error of mapping this data to the vertices of a zero centered binary hypercube and propose a simple and efficient alternating minimization algorithm to accomplish this task this algorithm dubbed iterative quantization itq has connections to multiclass spectral clustering and to the orthogonal procrustes problem and it can be used both with unsupervised data embeddings such as pca and supervised embeddings such as canonical correlation analysis cca the resulting binary codes significantly outperform several other state of the art methods we also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to pca or cca finally we demonstrate an application of itq to learning binary attributes or classemes on the imagenet data set

fast coding of feature vectors using neighbor to neighbor search searching for matches to high dimensional vectors using hard soft vector quantization is the most computationally expensive part of various computer vision algorithms including the bag of visual word bow this paper proposes a fast computation method neighbor to neighbor ntn search 1 which skips some calculations based on the similarity of input vectors for example in image classification using dense sift descriptors the ntn search seeks similar descriptors from a point on a grid to an adjacent point applications of the ntn search to vector quantization a gaussian mixture model sparse coding and a kernel codebook for extracting image or video representation are presented in this paper we evaluated the proposed method on image and video benchmarks the pascal voc 2007 classification challenge and the trecvid 2010 semantic indexing task ntn vq reduced the coding cost by 77 4 percent and ntn gmm reduced it by 89 3 percent without any significant degradation in classification performance

a reduced order extrapolation algorithm based on sfve method and pod technique for non stationary stokes equations in this paper a reduced order extrapolation algorithm roea based on proper orthogonal decomposition pod technique and classical stabilized finite volume element sfve method for non stationary stokes equations is established the error estimates between the roea solutions and the classical sfve solutions and the implementation for solving roea are provided some numerical examples are used to verify that the results of numerical computation are consistent with theoretical conclusions moreover it is shown that roea based on sfve formulation and pod method is feasible and efficient for solving non stationary stokes equations

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

proper general decomposition pgd for the resolution of navier stokes equations in this work the pgd method will be considered for solving some problems of fluid mechanics by looking for the solution as a sum of tensor product functions in the first stage the equations of stokes and burgers will be solved then we will solve the navier stokes problem in the case of the lid driven cavity for different reynolds numbers re 100 1000 and 10 000 finally the pgd method will be compared to the standard resolution technique both in terms of cpu time and accuracy

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

human action recognition with graph based multiple instance learning a new approach to human action recognition from realistic videos is presented in this paper first an affine motion model is utilized to compensate background motion for the purpose of extracting dense foreground trajectories then a trajectory spectral embedding is introduced to split up foreground action into multiple spatio temporal action parts for constructing a mid level representation to deal with over segmentation a novel density discontinuity detector is proposed for the sake of generating semantically salient action parts finally to handle the ambiguity in the training set action classification is formulated within the multiple instance learning framework which a spatio temporal graph model is incorporated into extensive experiments show that the proposed approach achieves competitive results to state of the art on ucf sports kisses slaps youtube and hollywood datasets a motion compensation approach based on affine model is introduced a density discontinuity detector is proposed to deal with over segmentation the bag of histograms is presented to develop the bag of features a graph based multiple instance learning method is proposed to handle ambiguity

full space local topology extraction for cross modal retrieval with the ever increasing availability of various kinds of multimedia data cross modal retrieval which enables information retrieval from various types of data given various types of query has become a research hotspot hashing based techniques have been developed to solve this problem however most previous works cannot capture the shared underlying structure of real world multimodal data which degrades their retrieval performances in this paper we propose a novel hashing method based on the extraction of the common manifold structure shared among different feature spaces to faithfully represent the common structure two kinds of local topology information are exploited in our method local angles are incorporated within the extraction of local topology of each feature space which is then used to learn a common intermediate subspace after heterogeneous features being embedded into this subspace local similarities are exploited to extract the local topology between different feature spaces and learn compact hamming embeddings to facilitate cross modal retrieval the proposed method is referred to as full space local topology extraction for hashing extensive comparisons with other state of the art methods on three benchmark multimedia data sets demonstrate the superiority of our proposed method in terms of retrieval recall and search accuracy

a self contained automated methodology for optimal flow control this paper describes a self contained automated methodology for active flow control which couples the time dependent navier stokes system with an adjoint navier stokes system and optimality conditions from which optimal states i e unsteady flow fields and controls e g actuators may be determined the problem of boundary layer instability suppression through wave cancellation is used as the intital validation case to test the methodology here the objective of control is to match the stress vector along a portion of the boundary to a steady base flow control is effected through the injection or suction of fluid through a single orifice on the boundary the results demonstrate that instability suppression can be achieved without any a priori knowledge of the flow unsteadiness such as frequencies instability type etc the present methodology has been extended to three dimensions and may potentially be applied to separation control relaminarization and turbulence control applications using one to many sensors and actuators

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 reduced finite element formulation based on pod method for two dimensional solute transport problems abstract a proper orthogonal decomposition pod method is applied to a usual finite element fe formulation for two dimensional solute transport problems with real practical applied background such that it is reduced into a reduced fe formulation with lower dimensions and high enough accuracy the error estimates between the reduced pod fe solutions and the usual fe solutions are provided it is shown by numerical examples that the results of numerical computation are consistent with theoretical conclusions moreover it is also shown that this validates the feasibility and efficiency of pod fe method

human action recognition in unconstrained videos by explicit motion modeling human action recognition in unconstrained videos is a challenging problem with many applications most state of the art approaches adopted the well known bag of features representations generated based on isolated local patches or patch trajectories where motion patterns such as object object and object background relationships are mostly discarded in this paper we propose a simple representation aiming at modeling these motion relationships we adopt global and local reference points to explicitly characterize motion information so that the final representation is more robust to camera movements which widely exist in unconstrained videos our approach operates on the top of visual codewords generated on dense local patch trajectories and therefore does not require foreground background separation which is normally a critical and difficult step in modeling object relationships through an extensive set of experimental evaluations we show that the proposed representation produces a very competitive performance on several challenging benchmark data sets further combining it with the standard bag of features or fisher vector representations can lead to substantial improvements

structure preserving binary representations for rgb d action recognition in this paper we propose a novel binary local representation for rgb d video data fusion with a structure preserving projection our contribution consists of two aspects toacquire a general feature for the video data we convert the problem to describing the gradient fields of rgb and depth information of video sequences with the local fluxes of the gradient fields which include the orientation and the magnitude of the neighborhood of each point a new kind of continuous local descriptor called local flux feature lff is obtained then the lffs from rgb and depth channels are fused into a hamming space via the structure preserving projection spp specifically an orthogonal projection matrix is applied to preserve the pairwise structure with a shape constraint to avoid the collapse of data structure in the projected space furthermore a bipartite graph structure of data is taken into consideration which is regarded as a higher level connection between samples and classes than the pairwise structure of local features theextensive experiments show not only the high efficiency of binary codes and the effectiveness of combining lffs from rgb d channels via spp on various action recognition benchmarks of rgb d data but also the potential power of lff for general action recognition

defocus blur invariant scale space feature extractions we propose modifications to scale space feature extraction techniques scale invariant feature transform sift and speeded up robust features surfs that make the feature detection and description invariant to defocus blur specifically the scale space blob detection relies on the second derivative responses of images our analysis of circular defocus blur which sufficiently approximates a real camera blur kernel and its effect on scale space blob detection suggests that fourth derivative and not the usual second derivative is optimal for detecting the blurred blobs while multi scale descriptors of blurred blobs are effective at establishing correspondences between the blurred images the proposed defocus blur invariant dbi scale space feature extraction techniques which we refer to as dbi sift and dbi surf do not require image deblurring nor blur kernel estimation meaning that their accuracy does not depend on the quality of image deblurring we offer empirical evidence of blur invariance by establishing interest point correspondences between sharp or blurred reference images and blurred target images

codewords distribution based optimal combination of equal average equal variance equal norm nearest neighbor fast search algorithm for vector quantization encoding vector quantization encoding requires expensive time to find the closest codeword through the codebook for each input vector a fast search algorithm for encoding is proposed in this paper the multilevel elimination criterion is still derived from the three features mean variance and norm inequalities constraints but the order of the three inequalities constraints instead of the predefined order like other conventional multilevel elimination criterion is optimized to speed up the encoding procedure in the proposed algorithm the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width and the priority order at the second and or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features the experimental results demonstrate the effectiveness of the proposed algorithm

neighborhood discriminant hashing for large scale image retrieval with the proliferation of large scale community contributed images hashing based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency in this paper we propose a novel hashing method named neighborhood discriminant hashing ndh for short to implement approximate similarity search different from the previous work we propose to learn a discriminant hashing function by exploiting local discriminative information i e the labels of a sample can be inherited from the neighbor samples it selects the hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible while an information theoretic regularization is jointly exploited using maximum entropy principle as a consequence the learned hashing function is compact and nonredundant among bits while each bit is highly informative extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed ndh method over state of the art hashing techniques

proper orthogonal decomposition approach and error estimation of mixed finite element methods for the tropical pacific ocean reduced gravity model in this paper the tropical pacific ocean reduced gravity model is studied using the proper orthogonal decomposition pod technique of mixed finite element mfe method and an error estimate of pod approximate solution based on mfe method is derived pod is a model reduction technique for the simulation of physical processes governed by partial differential equations e g fluid flows or other complex flow phenomena it is shown by numerical examples that the error between pod approximate solution and reference solution is consistent with theoretical results thus validating the feasibility and efficiency of pod method

existence of mild solutions of some semilinear neutral fractional functional evolution equations with infinite delay we deal in this paper with the mild solution for fractional semilinear differential equations with infinite delay d ax t ax t f t x t bx t email protected 0 t x t email protected t email protected 0 with t 0 and 0 0 on a complex banach space x by means of classical fixed points methods

action recognition using nonnegative action component representation and sparse basis selection in this paper we propose using high level action units to represent human actions in videos and based on such units a novel sparse model is developed for human action recognition there are three interconnected components in our approach first we propose a new context aware spatial temporal descriptor named locally weighted word context to improve the discriminability of the traditionally used local spatial temporal descriptors second from the statistics of the context aware descriptors we learn action units using the graph regularized nonnegative matrix factorization which leads to a part based representation and encodes the geometrical information these units effectively bridge the semantic gap in action recognition third we propose a sparse model based on a joint l2 1 norm to preserve the representative items and suppress noise in the action units intuitively when learning the dictionary for action representation the sparse model captures the fact that actions from the same class share similar units the proposed approach is evaluated on several publicly available data sets the experimental results and analysis clearly demonstrate the effectiveness of the proposed approach

a reduced fve formulation based on pod method and error analysis for two dimensional viscoelastic problem abstract proper orthogonal decomposition pod method has been successfully used in the reduced order modeling of complex systems in this paper we extend the applications of pod method i e combine a classical finite volume element fve method with pod method to establish a reduced fve formulation with lower dimensions and sufficiently high accuracy for two dimensional viscoelastic problem with real practical applied background and analyze the errors between the reduced pod fve solution and the classical fve solution so as to provide scientific theoretic basis for service applications some numerical examples illustrate the fact that the results of numerical computation are consistent with theoretical conclusions moreover it is also shown that the reduced fve formulation based on pod method is feasible and efficient for solving two dimensional viscoelastic problem

using operational matrix of two dimensional bernstein polynomials for solving two dimensional integral equations of fractional order in this paper we first preset two dimensional bernstein polynomials operational matrix 2dbpom for two dimensional fractional integral equations 2dfie then we introduce a computational method based on 2dbpom to reduce the 2dfie to those of solving a system of algebraic equations also we give the error analysis finally this method is applied for solving three examples to illustrate the applicability and accuracy of presented method

batch orthogonal locality sensitive hashing for angular similarity sign random projection locality sensitive hashing srp lsh is a widely used hashing method which provides an unbiased estimate of pairwise angular similarity yet may suffer from its large estimation variance we propose in this work batch orthogonal locality sensitive hashing bolsh as a significant improvement of srp lsh instead of independent random projections bolsh makes use of batch orthogonalized random projections i e we divide random projection vectors into several batches and orthogonalize the vectors in each batch respectively these batch orthogonalized random projections partition the data space into regular regions and thus provide a more accurate estimator we prove theoretically that bolsh still provides an unbiased estimate of pairwise angular similarity with a smaller variance for any angle in 0 compared with srp lsh furthermore we give a lower bound on the reduction of variance the extensive experiments on real data well validate that with the same length of binary code bolsh may achieve significant mean squared error reduction in estimating pairwise angular similarity moreover bolsh shows the superiority in extensive approximate nearest neighbor ann retrieval experiments

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