clique descriptor of affine invariant regions for robust wide baseline image matching assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small recent image matching algorithms have focused on affine invariant region ir detection and its description to increase the robustness in matching however the distinctiveness of an intensity based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern to address this problem we investigated the geometry of a local ir cluster also called a clique and propose a new clique based image matching method in the proposed method the clique of an ir is estimated by delaunay triangulation in a local affine frame and the hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors we also introduce two adaptively weighted clique distances where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution experimental results show the clique based matching method produces more tentative correspondences than variants of the sift based method

variational learning of a dirichlet process of generalized dirichlet distributions for simultaneous clustering and feature selection this paper introduces a novel enhancement for unsupervised feature selection based on generalized dirichlet gd mixture models our proposal is based on the extension of the finite mixture model previously developed in 1 to the infinite case via the consideration of dirichlet process mixtures which can be viewed actually as a purely nonparametric model since the number of mixture components can increase as data are introduced the infinite assumption is used to avoid problems related to model selection i e determination of the number of clusters and allows simultaneous separation of data in to similar clusters and selection of relevant features our resulting model is learned within a principled variational bayesian framework that we have developed the experimental results reported for both synthetic data and real world challenging applications involving image categorization automatic semantic annotation and retrieval show the ability of our approach to provide accurate models by distinguishing between relevant and irrelevant features without over or under fitting the data

kernel estimation and model combination in a bandit problem with covariates multi armed bandit problem is an important optimization game that requires an exploration exploitation tradeoff to achieve optimal total reward motivated from industrial applications such as online advertising and clinical research we consider a setting where the rewards of bandit machines are associated with covariates and the accurate estimation of the corresponding mean reward functions plays an important role in the performance of allocation rules under a exible problem setup we establish asymptotic strong consistency and perform a finite time regret analysis for a sequential randomized allocation strategy based on kernel estimation in addition since many nonparametric and parametric methods in supervised learning may be applied to estimating the mean reward functions but guidance on how to choose among them is generally unavailable we propose a model combining allocation strategy for adaptive performance simulations and a real data evaluation are conducted to illustrate the performance of the proposed allocation strategy

a representation for shape based on peaks and ridges in the difference of low pass transform this paper defines a multiple resolution representation for the two dimensional gray scale shapes in an image this representation is constructed by detecting peaks and ridges in the difference of lowpass dolp transform descriptions of shapes which are encoded in this representation may be matched efficiently despite changes in size orientation or position motivations for a multiple resolution representation are presented first followed by the definition of the dolp transform techniques are then presented for encoding a symbolic structural description of forms from the dolp transform this process involves detecting local peaks and ridges in each bandpass image and in the entire three dimensional space defined by the dolp transform linking adjacent peaks in different bandpass images gives a multiple resolution tree which describes shape peaks which are local maxima in this tree provide landmarks for aligning manipulating and matching shapes detecting and linking the ridges in each dolp bandpass image provides a graph which links peaks within a shape in a bandpass image and describes the positions of the boundaries of the shape at multiple resolutions detecting and linking the ridges in the dolp three space describes elongated forms and links the largest peaks in the tree the principles for determining the correspondence between symbols in pairs of such descriptions are then described such correspondence matching is shown to be simplified by using the correspondence at lower resolutions to constrain the possible correspondence at higher resolutions

scalable large margin mahalanobis distance metric learning for many machine learning algorithms such as k nearest neighbor k nn classifiers and k means clustering often their success heavily depends on the metric used to calculate distances between different data points an effective solution for defining such a metric is to learn it from a set of labeled training samples in this work we propose a fast and scalable algorithm to learn a mahalanobis distance metric the mahalanobis metric can be viewed as the euclidean distance metric on the input data that have been linearly transformed by employing the principle of margin maximization to achieve better generalization performances this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite p s d matrix is the unknown variable based on an important theorem that a p s d trace one matrix can always be represented as a convex combination of multiple rank one matrices our algorithm accommodates any differentiable loss function and solves the resulting optimization problem using a specialized gradient descent procedure during the course of optimization the proposed algorithm maintains the positive semidefiniteness of the matrix variable that is essential for a mahalanobis metric compared with conventional methods like standard interior point algorithms or the special solver used in large margin nearest neighbor our algorithm is much more efficient and has a better performance in scalability experiments on benchmark data sets suggest that compared with state of the art metric learning algorithms our algorithm can achieve a comparable classification accuracy with reduced computational complexity

co transduction for shape retrieval in this paper we propose a new shape object retrieval algorithm namely co transduction the performance of a retrieval system is critically decided by the accuracy of adopted similarity measures distances or metrics in shape object retrieval ideally intraclass objects should have smaller distances than interclass objects however it is a difficult task to design an ideal metric to account for the large intraclass variation different types of measures may focus on different aspects of the objects for example measures computed based on contours and skeletons are often complementary to each other our goal is to develop an algorithm to fuse different similarity measures for robust shape retrieval through a semisupervised learning framework we name our method co transduction which is inspired by the co training algorithm given two similarity measures and a query shape the algorithm iteratively retrieves the most similar shapes using one measure and assigns them to a pool for the other measure to do a re ranking and vice versa using co transduction we achieved an improved result of 97 72 bull s eye measure on the mpeg 7 data set over the state of the art performance we also present an algorithm called tri transduction to fuse multiple input similarities and it achieved 99 06 on the mpeg 7 data set our algorithm is general and it can be directly applied on input similarity measures metrics it is not limited to object shape retrieval and can be applied to other tasks for ranking retrieval

study of a robust feature the pointwise lipschitz regularity the aim of this paper is to highlight the relevance in computer vision of the pointwise lipschitz regularity the regularity gives a measure of the local regularity of the intensity function associated to an image known wavelet methods provide an efficient computation of at contour points of the image from a theoretical point of view we study the effect of geometric deformations and other specific transformations applied to the image showing invariance properties from a practical point of view we assess the robustness of the regularity when the image undergoes various transformations the results we obtain show the lipschitz regularity is a suitable feature for applications in computer vision

non asymptotic analysis of a new bandit algorithm for semi bounded rewards in this paper we consider a stochastic multiarmed bandit problem it is known in this problem that deterministic minimum empirical divergence dmed policy achieves the asymptotic theoretical bound for the model where each reward distribution is supported in a known bounded interval say 0 1 however the regret bound of dmed is described in an asymptotic form and the performance in finite time has been unknown we modify this policy and derive a finite time regret bound for the new policy indexed minimum empirical divergence imed by refining large deviation probabilities to a simple nonasymptotic form further the refined analysis reveals that the finite time regret bound is valid even in the case that the reward is not bounded from below therefore our finite time result applies to the case that the minimum reward that is the maximum loss is unknown or unbounded we also present some simulation results which shows that imed much improves dmed and performs competitively to other state of the art policies

rasim a novel rotation and scale invariant matching of local image interest points this paper presents a novel algorithm for matching image interest points potential interest points are identified by searching for local peaks in difference of gaussian dog images we refine and assign rotation scale and location for each keypoint by using the sift algorithm pseudo log polar sampling grid is then applied to properly scaled image patches around each keypoint and a weighted adaptive lifting scheme transform is designed for each ring of the log polar grid the designed adaptive transform for a ring in the reference keypoint and the general non adaptive transform are applied to the corresponding ring in a test keypoint similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non adaptive transforms we refer to the proposed versatile system of rotation and scale invariant matching as rasim our experiments show that the accuracy of rasim is more than sift which is the most widely used interest point matching algorithm in the literature rasim is also more robust to image deformations while its computation time is comparable to sift

ray projection for recovering projective transformations and illumination changes the ray projection and its application to recovering a projective geometric transformation and an affine lighting change between two objects are mathematically studied a novel technique viz variable contour is proposed for accurately evaluating the ray projection moreover a novel flexible framework of the ray projection is devised for the joint recovery of the eight parameters for the projective transformation and the two parameters for the lighting change finally the framework is experimentally evaluated by recovering a variety of geometric and photometric transformations between real images of indoor and outdoor scenes its robustness to image blur and occlusion is demonstrated its versatility is illustrated through matching different objects in different classes

feature based sequence to sequence matching this paper studies the problem of matching two unsynchronized video sequences of the same dynamic scene recorded by different stationary uncalibrated video cameras the matching is done both in time and in space where the spatial matching can be modeled by a homography for 2d scenarios or by a fundamental matrix for 3d scenarios our approach is based on matching space time trajectories of moving objects in contrast to matching interest points e g corners as done in regular feature based image to image matching techniques the sequences are matched in space and time by enforcing consistent matching of all points along corresponding space time trajectories by exploiting the dynamic properties of these space time trajectories we obtain sub frame temporal correspondence synchronization between the two video sequences furthermore using trajectories rather than feature points significantly reduces the combinatorial complexity of the spatial point matching problem when the search space is large this benefit allows for matching information across sensors in situations which are extremely difficult when only image to image matching is used including a matching under large scale zoom differences b very wide base line matching and c matching across different sensing modalities e g ir and visible light cameras we show examples of recovering homographies and fundamental matrices under such conditions

scale affine invariant interest point detectors in this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations our scale and affine invariant detectors are based on the following recent results 1 interest points extracted with the harris detector can be adapted to affine transformations and give repeatable results geometrically stable 2 the characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives the laplacian 3 the affine shape of a point neighborhood is estimated based on the second moment matrix our scale invariant detector computes a multi scale representation for the harris interest point detector and then selects points at which a local measure the laplacian is maximal over scales this provides a set of distinctive points which are invariant to scale rotation and translation as well as robust to illumination changes and limited changes of viewpoint the characteristic scale determines a scale invariant region for each point we extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood an iterative algorithm modifies location scale and neighborhood of each point and converges to affine invariant points this method can deal with significant affine transformations including large scale changes the characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point we present a comparative evaluation of different detectors and show that our approach provides better results than existing methods the performance of our detector is also confirmed by excellent matching resultss the image is described by a set of scale affine invariant descriptors computed on the regions associated with our points

matching images with multiple descriptors an unsupervised approach for locally adaptive descriptor selection with the aim to improve the performance of feature matching we present an unsupervised approach for adaptive description selection in the space of homographies inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain our approach stands on the unsupervised nature of feature matching and can choose a good descriptor locally for matching each feature point instead of using one global descriptor to this end the homography space serves as the domain for selecting various heterogeneous descriptors correspondences obtained by any descriptors are considered as points in the space and their geometric coherence and spatial continuity are measured via computing the geodesic distances in this way mutual verification across different descriptors is allowed and correct correspondences will be highlighted with a high degree of consistency short geodesic distances here it follows that one class svm can be applied to identifying these correct correspondences and achieves adaptive descriptor selection the proposed approach is comprehensively compared with the state of the art approaches and evaluated on five benchmarks of image matching the promising results manifest its effectiveness

global propagation of affine invariant features for robust matching local invariant features have been successfully used in image matching to cope with viewpoint change partial occlusion and clutters however when these factors become too strong there will be a lot of mismatches due to the limited repeatability and discriminative power of features in this paper we present an efficient approach to remove the false matches and propagate the correct ones for the affine invariant features which represent the state of the art local invariance first a pair wise affine consistency measure is proposed to evaluate the consensus of the matches of affine invariant regions the measure takes into account both the keypoint location and the region shape size and orientation based on this measure a geometric filter is then presented which can efficiently remove the outliers from the initial matches and is robust to severe clutters and non rigid deformation to increase the correct matches we propose a global match refinement and propagation method that simultaneously finds a optimal group of local affine transforms to relate the features in two images the global method is capable of producing a quasi dense set of matches even for the weakly textured surfaces that suffer strong rigid transformation or non rigid deformation the strong capability of the proposed method in dealing with significant viewpoint change non rigid deformation and low texture objects is demonstrated in experiments of image matching object recognition and image based rendering

geometric information criterion for model selection in building a 3 d model of the environment from image and sensor data one must fit to the data an appropriate class of models which can be regarded as a parametrized manifold or geometric model defined in the data space in this paper we present a statistical framework for detecting degeneracies of a geometric model by evaluating its predictive capability in terms of the expected residual and derive the geometric aic we show that it allows us to detect singularities in a structure from motion analysis without introducing any empirically adjustable thresholds we illustrate our approach by simulation examples we also discuss the application potential of this theory for a wide range of computer vision and robotics problems

feature vector field and feature matching in this paper we propose a feature vector field for images which is built by the inner products and exterior products of image gradients the feature vector field effectively represents image edges and feature points including corners and edge points with big curvature using the feature vector field some novel descriptors are constructed for point matching and curve matching the descriptors are all invariant to image euclidean transformations and linear intensity changes the experiments show that the descriptors also have a good adaptability to small image affine transformation jpeg compression and nonlinear changes of intensity

the design and use of steerable filters the authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters allowing one to adaptively steer a filter to any orientation and to determine analytically the filter output as a function of orientation steerable filters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation the authors show how to design and steer the filters and present examples of their use in the analysis of orientation and phase angularly adaptive filtering edge detection and shape from shading one can also build a self similar steerable pyramid representation the same concepts can be generalized to the design of 3 d steerable filters

co segmentation guided hough transform for robust feature matching we present an algorithm that integrates image co segmentation into feature matching and can robustly yield accurate and dense feature correspondences inspired by the fact that correct feature correspondences on the same object typically have coherent transformations we cast the task of feature matching as a density estimation problem in the homography space specifically we project the homographies of correspondence candidates into the parametric hough space in which geometric verification of correspondences can be activated by voting the precision of matching is then boosted on the other hand we leverage image co segmentation which discovers object boundaries to determine relevant voters and speed up hough voting in addition correspondence enrichment can be achieved by inferring the concerted homographies that are propagated between the features within the same segments the recall is hence increased in our approach feature matching and image co segmentation are tightly coupled through an iterative optimization process more and more correct correspondences are detected owing to object boundaries revealed by co segmentation the proposed approach is comprehensively evaluated promising experimental results on four datasets manifest its effectiveness

scale rotation invariant pattern entropy for keypoint based near duplicate detection near duplicate nd detection appears as a timely issue recently being regarded as a powerful tool for various emerging applications in the web 2 0 environment particularly the identification of near duplicates enables the tasks such as copyright enforcement news topic tracking image and video search in this paper we describe an algorithm namely scale rotation invariant pattern entropy sr pe for the detection of near duplicates in large scale video corpus sr pe is a novel pattern evaluation technique capable of measuring the spatial regularity of matching patterns formed by local keypoints more importantly the coherency of patterns and the perception of visual similarity under the scenario that there could be multiple nd regions undergone arbitrary transformations respectively are carefully addressed through entropy measure to demonstrate our work in large scale dataset a practical framework composed of three components bag of words representation local keypoint matching and sr pe evaluation is also proposed for the rapid detection of near duplicates

sub gaussian mean estimators we discuss the possibilities and limitations of estimating the mean of a real valued random variable from independent and identically distributed observations from a non asymptotic point of view in particular we define estimators with a sub gaussian behavior even for certain heavy tailed distributions we also prove various impossibility results for mean estimators

a performance evaluation of local descriptors in this paper we compare the performance of descriptors computed for local interest regions as for example extracted by the harris affine detector mikolajczyk k and schmid c 2004 many different descriptors have been proposed in the literature it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector the descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations we compare shape context belongie s et al april 2002 steerable filters freeman w and adelson e setp 1991 pca sift ke y and sukthankar r 2004 differential invariants koenderink j and van doorn a 1987 spin images lazebnik s et al 2003 sift lowe d g 1999 complex filters schaffalitzky f and zisserman a 2002 moment invariants van gool l et al 1996 and cross correlation for different types of interest regions we also propose an extension of the sift descriptor and show that it outperforms the original method furthermore we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the sift based descriptors perform best moments and steerable filters show the best performance among the low dimensional descriptors

a computational approach for corner and vertex detection corners and vertexes are strong and useful features in computer vision for scene analysis stereo matching and motion analysis here we deal with the development of a computational approach to these important features we consider first a corner model and study analytically its behavior once it has been smoothed using the well known gaussian filter this allows us to clarify the behavior of some well knowncornerness measure based approaches used to detect these points of interest most of these classical approaches appear to detect points that do not correspond to the exact position of the corner a new scale space based approach that combines useful properties from the laplacian and beaudet s measure beaudet 1978 is then proposed in order to correct and detect exactly the corner position an extension of this approach is then developed to solve the problem of trihedral vertex characterization and detection in particular it is shown that a trihedral vertex has two elliptic maxima on extremal contrast surfaces if the contrast is sufficient and this allows us to classify trihedral vertexes in 2 classes vertex and vertex as corner the corner detection approach developed is applied to accurately detect trihedral vertexes using an additional test in order to make a distinction between trihedral vertexes and corners many experiments have been carried out using noisy synthetic data and real images containing corners and vertexes most of the promising results obtained are used to illustrate the experimental section of this paper

common visual pattern discovery via nonlinear mean shift clustering discovering common visual patterns cvps from two images is a challenging task due to the geometric and photometric deformations as well as noises and clutters the problem is generally boiled down to recovering correspondences of local invariant features and the conventionally addressed by graph based quadratic optimization approaches which often suffer from high computational cost in this paper we propose an efficient approach by viewing the problem from a novel perspective in particular we consider each cvp as a common object in two images with a group of coherently deformed local regions a geometric space with matrix lie group structure is constructed by stacking up transformations estimated from initially appearance matched local interest region pairs this is followed by a mean shift clustering stage to group together those close transformations in the space joining regions associated with transformations of the same group together within each input image forms two large regions sharing similar geometric configuration which naturally leads to a cvp to account for the non euclidean nature of the matrix lie group mean shift vectors are derived in the corresponding lie algebra vector space with a newly provided effective distance measure extensive experiments on single and multiple common object discovery tasks as well as near duplicate image retrieval verify the robustness and efficiency of the proposed approach

trees and hairs for some hyperbolic entire maps of finite order let f be an entire transcendental map of finite order such that all the singularities of f 1 are contained in a compact subset of the immediate basin b of an attracting fixed point it is proved that there exist geometric coding trees of preimages of points from b with all branches convergent to points from hat mathbb c this implies that the riemann map onto b has radial limits everywhere moreover the julia set of f consists of disjoint curves hairs tending to infinity homeomorphic to a half line composed of points with a given symbolic itinerary and attached to the unique point accessible from b endpoint of the hair these facts generalize the corresponding results for exponential maps

spectral symmetry analysis we present a spectral approach for detecting and analyzing rotational and reflectional symmetries in n dimensions our main contribution is the derivation of a symmetry detection and analysis scheme for sets of points irn and its extension to image analysis by way of local features each object is represented by a set of points s irn where the symmetry is manifested by the multiple self alignments of s the alignment problem is formulated as a quadratic binary optimization problem with an efficient solution via spectral relaxation for symmetric objects this results in a multiplicity of eigenvalues whose corresponding eigenvectors allow the detection and analysis of both types of symmetry we improve the scheme s robustness by incorporating geometrical constraints into the spectral analysis our approach is experimentally verified by applying it to 2d and 3d synthetic objects as well as real images

performance comparisons of contour based corner detectors corner detectors have many applications in computer vision and image identification and retrieval contour based corner detectors directly or indirectly estimate a significance measure e g curvature on the points of a planar curve and select the curvature extrema points as corners while an extensive number of contour based corner detectors have been proposed over the last four decades there is no comparative study of recently proposed detectors this paper is an attempt to fill this gap the general framework of contour based corner detection is presented and two major issues curve smoothing and curvature estimation which have major impacts on the corner detection performance are discussed a number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets it is observed that while the detectors using indirect curvature estimation techniques are more robust the detectors using direct curvature estimation techniques are faster

discriminative learning of local image descriptors in this paper we explore methods for learning local image descriptors from training data we describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest neighbor classifier we consider both linear and nonlinear transforms with dimensionality reduction and make use of discriminant learning techniques such as linear discriminant analysis lda and powell minimization to solve for the parameters using these techniques we obtain descriptors that exceed state of the art performance with low dimensionality in addition to new experiments and recommendations for descriptor learning we are also making available a new and realistic ground truth data set based on multiview stereo data

corner detection and classification using anisotropic directional derivative representations this paper proposes a corner detector and classifier using anisotropic directional derivative andd representations the andd representation at a pixel is a function of the oriented angle and characterizes the local directional grayscale variation around the pixel the proposed corner detector fuses the ideas of the contour and intensity based detection it consists of three cascaded blocks first the edge map of an image is obtained by the canny detector and from which contours are extracted and patched next the andd representation at each pixel on contours is calculated and normalized by its maximal magnitude the area surrounded by the normalized andd representation forms a new corner measure finally the nonmaximum suppression and thresholding are operated on each contour to find corners in terms of the corner measure moreover a corner classifier based on the peak number of the andd representation is given experiments are made to evaluate the proposed detector and classifier the proposed detector is competitive with the two recent state of the art corner detectors the he yung detector and cpda detector in detection capability and attains higher repeatability under affine transforms the proposed classifier can discriminate effectively simple corners y type corners and higher order corners

a comparison of affine region detectors the paper gives a snapshot of the state of the art in affine covariant region detectors and compares their performance on a set of test images under varying imaging conditions six types of detectors are included detectors based on affine normalization around harris mikolajczyk and schmid 2002 schaffalitzky and zisserman 2002 and hessian points mikolajczyk and schmid 2002 a detector of maximally stable extremal regions proposed by matas et al 2002 an edge based region detector tuytelaars and van gool 1999 and a detector based on intensity extrema tuytelaars and van gool 2000 and a detector of salient regions proposed by kadir zisserman and brady 2004 the performance is measured against changes in viewpoint scale illumination defocus and image compression the objective of this paper is also to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework

arranging and interpolating sparse unorganized feature points with geodesic circular arc a novel method to reconstruct object boundaries with geodesic circular arc is proposed in this paper within this framework an energy of circular arc spline is utilized to simultaneously arrange and interpolate each member in the set of sparse unorganized feature points from the desired boundaries a general form for a family of parametric circular arc spline is firstly derived and followed by a novel method of arranging these feature points by minimizing an energy term depending on the circular arc spline configuration defined on these feature points with regard to the fact that the energy function is usually nonconvex and nondifferentiable at its critical points an improved scheme of particle swarm optimizer is given to find the minimum for the energy in this paper with this improved scheme each pair of neighboring feature points along the boundaries of the desired objects are picked out from the set of sparse unorganized feature points and the corresponding directional chord tangent angles are computed simultaneously to finish interpolation we show experimentally and comparatively that the proposed method can perform effectively to restrict leakage on weak boundaries and premature convergence on long concave boundaries besides it has good noise robustness and can as well extract multiple and open boundaries

affine transforms between image space and color space for invariant local descriptors accurate local region description is a keypoint in many applications and has been the topic of lots of recent papers starting from the very accurate sift most of the approaches exploit the local gradient information that suffers from several drawbacks first it is unstable in case of severe geometry distortions second it cannot be easily summarized in a compact way and third it is not designed to account vectorial color information in this paper we propose an alternative by designing compact descriptors that account both the colors present in the region and their spatial distribution each pixel being characterized by five coordinates two in the image space and three in the color space we try to evaluate affine transforms that allow to go from the spatial coordinates to the color coordinates and inversely obviously such kind of transform does not exist but we show that after applying it to the original coordinates the resulted positions are both discriminative and invariant to many acquisition conditions hence depending on the original space image or color and the destination space color or image we design different complementary descriptors their discriminative power and invariance properties are assessed and compared with the best color descriptors in the context of region matching and object classification

retrieval of images of man made structures based on projective invariance in this paper we propose a geometry based image retrieval scheme that makes use of projectively invariant features cross ratio cr is an invariant feature under projective transformations for collinear points we compute the crs of point sets in quadruplets and the cr histogram is used as the feature for retrieval purposes being a geometric feature it allows us to retrieve similar images irrespective of view point and illumination changes we can retrieve the same building even if the facade has undergone a fresh coat of paints color and textural features can also be included if desired experimental results show a favorably very good retrieval accuracy when tested on an image database of size 4000 the method is very effective in retrieving images having man made objects rich in polygonal structures like buildings rail tracks etc

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

geometric latent dirichlet allocation on a matching graph for large scale image datasets given a large scale collection of images our aim is to efficiently associate images which contain the same entity for example a building or object and to discover the significant entities to achieve this we introduce the geometric latent dirichlet allocation glda model for unsupervised discovery of particular objects in unordered image collections this explicitly represents images as mixtures of particular objects or facades and builds rich latent topic models which incorporate the identity and locations of visual words specific to the topic in a geometrically consistent way applying standard inference techniques to this model enables images likely to contain the same object to be probabilistically grouped and ranked additionally to reduce the computational cost of applying the glda model to large datasets we propose a scalable method that first computes a matching graph over all the images in a dataset this matching graph connects images that contain the same object and rough image groups can be mined from this graph using standard clustering techniques the glda model can then be applied to generate a more nuanced representation of the data we also discuss how hub images images representative of an object or landmark can easily be extracted from our matching graph representation we evaluate our techniques on the publicly available oxford buildings dataset 5k images and show examples of automatically mined objects the methods are evaluated quantitatively on this dataset using a ground truth labeling for a number of oxford landmarks to demonstrate the scalability of the matching graph method we show qualitative results on two larger datasets of images taken of the statue of liberty 37k images and rome 1m images

empirical risk minimization for heavy tailed losses the purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails in such situations usual empirical averages may fail to provide reliable estimates and empirical risk minimization may provide large excess risk however some robust mean estimators proposed in the literature may be used to replace empirical means in this paper we investigate empirical risk minimization based on a robust estimate proposed by catoni we develop performance bounds based on chaining arguments tailored to catoni s mean estimator

wavelet domain multifractal analysis for static and dynamic texture classification in this paper we propose a new texture descriptor for both static and dynamic textures the new descriptor is built on the wavelet based spatial frequency analysis of two complementary wavelet pyramids standard multiscale and wavelet leader these wavelet pyramids essentially capture the local texture responses in multiple high pass channels in a multiscale and multiorientation fashion in which there exists a strong power law relationship for natural images such a power law relationship is characterized by the so called multifractal analysis in addition two more techniques scale normalization and multiorientation image averaging are introduced to further improve the robustness of the proposed descriptor combining these techniques the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes we apply the descriptor for classifying both static and dynamic textures our method has demonstrated excellent performance in comparison with the state of the art approaches in several public benchmark datasets

robust imagegraph rank level feature fusion for image search recently feature fusion has demonstrated its effectiveness in image search however bad features and inappropriate parameters usually bring about false positive images i e outliers leading to inferior performance therefore a major challenge of fusion scheme is how to be robust to outliers towards this goal this paper proposes a rank level framework for robust feature fusion first we define rank distance to measure the relevance of images at rank level based on it bayes similarity is introduced to evaluate the retrieval quality of individual features through which true matches tend to obtain higher weight than outliers then we construct the directed imagegraph to encode the relationship of images each image is connected to its k nearest neighbors with an edge and the edge is weighted by bayes similarity multiple rank lists resulted from different methods are merged via imagegraph furthermore on the fused imagegraph local ranking is performed to re order the initial rank lists it aims at local optimization and thus is more robust to global outliers extensive experiments on four benchmark data sets validate the effectiveness of our method besides the proposed method outperforms two popular fusion schemes and the results are competitive to the state of the art

content based image retrieval using visually significant point features this paper presents a new image retrieval scheme using visually significant point features the clusters of points around significant curvature regions high medium and weak type are extracted using a fuzzy set theoretic approach some invariant color features are computed from these points to evaluate the similarity between images a set of relevant and non redundant features is selected using the mutual information based minimum redundancy maximum relevance framework the relative importance of each feature is evaluated using a fuzzy entropy based measure which is computed from the sets of retrieved images marked relevant and irrelevant by the users the performance of the system is evaluated using different sets of examples from a general purpose image database the robustness of the system is also shown when the images undergo different transformations

contrast invariant interest point detection by zero norm log filter the laplacian of gaussian log filter is widely used in interest point detection however low contrast image structures though stable and significant are often submerged by the high contrast ones in the response image of the log filter and hence are difficult to be detected to solve this problem we derive a generalized log filter and propose a zero norm log filter the response of the zero norm log filter is proportional to the weighted number of bright dark pixels in a local region which makes this filter be invariant to the image contrast based on the zero norm log filter we develop an interest point detector to extract local structures from images compared with the contrast dependent detectors such as the popular scale invariant feature transform detector the proposed detector is robust to illumination changes and abrupt variations of images experiments on benchmark databases demonstrate the superior performance of the proposed zero norm log detector in terms of the repeatability and matching score of the detected points as well as the image recognition rate under different conditions

positive semidefinite metric learning using boosting like algorithms the success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data it is often beneficial to learn such a metric from the input training data instead of using a default one such as the euclidean distance in this work we propose a boosting based technique termed boostmetric for learning a quadratic mahalanobis distance metric learning a valid mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive semidefinite semidefinite programming is often used to enforce this constraint but does not scale well and is not easy to implement boostmetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace one rank one matrices boostmetric thus uses rank one positive semidefinite matrices as weak learners within an efficient and scalable boosting based learning process the resulting methods are easy to implement efficient and can accommodate various types of constraints we extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor experiments on various data sets demonstrate that the proposed algorithms compare favorably to those state of the art methods in terms of classification accuracy and running time

one dimensional mapping for estimating projective transformations in this paper the 1 d mapping is studied for estimating a general projective transformation between images first a ray model of image patches is proposed to convert a complex projective deformation of a patch in the cartesian space into two simple rigid shifts of radial lines in the log polar inverse polar space to capture these shifts via direct search two types of radial image line matching viz 1 d log polar mapping and 1 d inverse polar mapping are formally defined also the unique features of these shifts are analyzed then a novel two step mathematical framework is devised for transformation estimation in the first step the 1 d log polar mapping is applied to estimating four affine parameters via two alternative methods for simulated real images in the second step the 1 d inverse polar mapping is applied to estimating two projective parameters by a direct search combined with the two step line matching two translational parameters are also determined finally the performance of the framework is evaluated first in estimating a variety of projective transformations by comparing the estimates with their ground truths and between the two proposed methods and second in registering a wide range of challenging image pairs by comparing with a state of the art feature based method

extremal regions detection guided by maxima of gradient magnitude a problem of computer vision applications is to detect regions of interest under different imaging conditions the state of the art maximally stable extremal regions msers detects affine covariant regions by applying all possible thresholds on the input image and through three main steps including 1 making a component tree of extremal regions evolution 2 obtaining region stability criterion and 3 cleaning up the mser performs very well but it does not consider any information about the boundaries of the regions which are important for detecting repeatable extremal regions we have shown in this paper that employing prior information about boundaries of regions results in a novel region detector algorithm that not only outperforms mser but avoids the mser s rather complicated steps of enumeration and the cleaning up to employ the information about the region boundaries we introduce maxima of gradient magnitudes mgms which are shown to be points that are mostly around the boundaries of the regions having found the mgms the method obtains a global criterion for each level of the input image which is used to find extremum levels els the found els are then used to detect extremal regions the proposed algorithm which is called extremal regions of extremum levels erel has been tested on the public benchmark data set of mikolajczyk the obtained experimental results show that the inclusion of region boundaries through mgms results in a detector that detects regions with high repeatability scores and is more robust against noise compared with mser

affine covariant features for fisheye distortion local modeling perspective cameras are the most popular imaging sensors used in computer vision however many application fields including automotive surveillance and robotics require the use of wide angle cameras e g fisheye which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images affine covariant feature detectors have proved successful in a variety of computer vision applications including object recognition image registration and visual search moreover their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature in this paper we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses as theoretical outcome we show that the inherently non linear radial distortion can be locally approximated by linear functions with a reasonably small error the experimental analysis builds on mikolajczyk s benchmark to assess the robustness of three popular affine region detectors i e maximally stable extremal regions and harris and hessian affine region detectors with respect to different variabilities as well as to radial distortion to support the evaluations we rely on the oxford data set and introduce a novel benchmark data set comprising 50 images depicting different scene categories experiments are carried out on rectilinear images to which radial distortion is artificially added and on real world images acquired using fisheye lenses our analysis points out that affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modeled as an additional affine variability

gaussian processes for object categorization discriminative methods for visual object category recognition are typically non probabilistic predicting class labels but not directly providing an estimate of uncertainty gaussian processes gps provide a framework for deriving regression techniques with explicit uncertainty models we show here how gaussian processes with covariance functions defined based on a pyramid match kernel pmk can be used for probabilistic object category recognition our probabilistic formulation provides a principled way to learn hyperparameters which we utilize to learn an optimal combination of multiple covariance functions it also offers confidence estimates at test points and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling we show that with an appropriate combination of kernels a significant boost in classification performance is possible further our experiments indicate the utility of active learning with probabilistic predictive models especially when the amount of training data labels that may be sought for a category is ultimately very small

scene oriented hierarchical classification of blurry and noisy images a system for scene oriented hierarchical classification of blurry and noisy images is proposed it attempts to simulate important features of the human visual perception the underlying approach is based on three strategies extraction of essential signatures captured from a global context simulating the global pathway highlight detection based on local conspicuous features of the reconstructed image simulating the local pathway and hierarchical classification of extracted features using probabilistic techniques the techniques involved in hierarchical classification use input from both the local and global pathways visual context is exploited by a combination of gabor filtering with the principal component analysis in parallel a pseudo restoration process is applied together with an affine invariant approach to improve the accuracy in the detection of local conspicuous features subsequently the local conspicuous features and the global essential signature are combined and clustered by a monte carlo approach finally clustered features are fed to a self organizing tree algorithm to generate the final hierarchical classification results selected representative results of a comprehensive experimental evaluation validate the proposed system

bandits with heavy tail the stochastic multiarmed bandit problem is well understood when the reward distributions are sub gaussian in this paper we examine the bandit problem under the weaker assumption that the distributions have moments of order 1 e for some e 0 1 surprisingly moments of order 2 i e finite variance are sufficient to obtain regret bounds of the same order as under sub gaussian reward distributions in order to achieve such regret we define sampling strategies based on refined estimators of the mean such as the truncated empirical mean catoni s m estimator and the median of means estimator we also derive matching lower bounds that also show that the best achievable regret deteriorates when e 1

dense correspondences across scenes and scales we seek a practical method for establishing dense correspondences between two images with similar content but possibly different 3d scenes one of the challenges in designing such a system is the local scale differences of objects appearing in the two images previous methods often considered only few image pixels matching only pixels for which stable scales may be reliably estimated recently others have considered dense correspondences but with substantial costs associated with generating storing and matching scale invariant descriptors our work is motivated by the observation that pixels in the image have contexts the pixels around them which may be exploited in order to reliably estimate local scales we make the following contributions i we show that scales estimated in sparse interest points may be propagated to neighboring pixels where this information cannot be reliably determined doing so allows scale invariant descriptors to be extracted anywhere in the image ii we explore three means for propagating this information using the scales at detected interest points using the underlying image information to guide scale propagation in each image separately and using both images together finally iii we provide extensive qualitative and quantitative results demonstrating that scale propagation allows for accurate dense correspondences to be obtained even between very different images with little computational costs beyond those required by existing methods

progressively weighted affine adaptive correlation matching for quasi dense 3d reconstruction correlation matching has been widely accepted as a rudimentary similarity measure to obtain dense 3d reconstruction from a stereo pair in particular given a large overlapping area between images with minimal scale differences the correlation results followed by a geometrically constrained global optimisation delivers adequately dense and accurate reconstruction results in order to achieve greater reliability however correlation matching should correctly account for the geometrical distortion introduced by the different viewing angles of the stereo or multi view sensors conventional adaptive least squares correlation alsc matching addresses this by modifying the shape of a matching window iteratively assuming that the distortion can be approximated by an affine transform nevertheless since an image captured from different viewing angle is often not practically identical due to scene occlusions the matching confidence normally deteriorates subsequently it affects the density of the reconstruction results from alsc based stereo region growing algorithms to address this we propose an advanced alsc matching method that can progressively update matching weight for each pixel in an aggregating window using a relaxation labelling technique the experimental results show that the proposed method can improve matching performance which consequently enhances the quality of stereo reconstruction also the results demonstrate its ability to refine a scale invariant conjugate point pair to an affine and scale invariant point pair

an optimal algorithm for monte carlo estimation a typical approach to estimate an unknown quantity mu is to design an experiment that produces a random variable z distributed in 0 1 with e z mu run this experiment independently a number of times and use the average of the outcomes as the estimate in this paper we consider the case when no a priori information about z is known except that is distributed in 0 1 we describe an approximation algorithm cal a cal a which given epsilon and delta when running independent experiments with respect to any z produces an estimate that is within a factor 1 epsilon of mu with probability at least 1 delta we prove that the expected number of experiments run by cal a cal a which depends on z is optimal to within a constant factor for every z

affine morphological multiscale analysis of corners andmultiple junctions in this paper we study the application of the affine morphological scale space amss to the analysis of singularities corners or multiple junctions of the shapes present in a 2 d image we introduce a new family of travelling wave solutions of amss which determines the evolution of the initial shapes given by conics we characterize the evolution of corners accross the scales according to their angle we develop a numerical algorithm to compute amss accross the scales and we present some experimental results about corners and multiple junction detection

robust estimation of high dimensional covariance and precision matrices high dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components covariance and precision matrices provide a useful summary of such structure yet the performance of popular matrix estimators typically hinges upon a sub gaussianity assumption this paper presents robust matrix estimators whose performance is guaranteed for a much richer class of distributions the proposed estimators under a bounded fourth moment assumption achieve the same minimax convergence rates as do existing methods under a sub gaussianity assumption consistency of the proposed estimators is also established under the weak assumption of bounded 2 e moments for e 0 2 the associated convergence rates depend on e

corner detection based on gradient correlation matrices of planar curves an efficient and novel technique is developed for detecting and localizing corners of planar curves this paper discusses the gradient feature distribution of planar curves and constructs gradient correlation matrices gcms over the region of support ros of these planar curves it is shown that the eigen structure and determinant of the gcms encode the geometric features of these curves such as curvature features and the dominant points the determinant of the gcms is shown to have a strong corner response and is used as a cornerness measure of planar curves a comprehensive performance evaluation of the proposed detector is performed using the acu and localization error criteria experimental results demonstrate that the gcm detector has a strong corner position response along with a high detection rate and good localization performance

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

local grayvalue invariants for image retrieval this paper addresses the problem of retrieving images from large image databases the method is based on local grayvalue invariants which are computed at automatically detected interest points a voting algorithm and semilocal constraints make retrieval possible indexing allows for efficient retrieval from a database of more than 1 000 images experimental results show correct retrieval in the case of partial visibility similarity transformations extraneous features and small perspective deformations

improving bag of features for large scale image search this article improves recent methods for large scale image search we first analyze the bag of features approach in the framework of approximate nearest neighbor search this leads us to derive a more precise representation based on hamming embedding he and weak geometric consistency constraints wgc he provides binary signatures that refine the matching based on visual words wgc filters matching descriptors that are not consistent in terms of angle and scale he and wgc are integrated within an inverted file and are efficiently exploited for all images in the dataset we then introduce a graph structured quantizer which significantly speeds up the assignment of the descriptors to visual words a comparison with the state of the art shows the interest of our approach when high accuracy is needed experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints as well as their efficiency estimation of the full geometric transformation i e a re ranking step on a short list of images is shown to be complementary to our weak geometric consistency constraints our approach is shown to outperform the state of the art on the three datasets

evaluation of interest point detectors many different low level feature detectors exist and it is widely agreed that the evaluation of detectors is important in this paper we introduce two evaluation criteria for interest points repeatability rate and information content repeatability rate evaluates the geometric stability under different transformations information content measures the distinctiveness of features different interest point detectors are compared using these two criteria we determine which detector gives the best results and show that it satisfies the criteria well

affinity learning with diffusion on tensor product graph in many applications we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples often the pairwise similarities which are also called affinities are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples as observed in several recent approaches more reliable similarities can be obtained if the original similarities are diffused in the context of other data points where the context of each point is a set of points most similar to it compared to the existing methods our approach differs in two main aspects first instead of diffusing the similarity information on the original graph we propose to utilize the tensor product graph tpg obtained by the tensor product of the original graph with itself since tpg takes into account higher order information it is not a surprise that we obtain more reliable similarities however it comes at the price of higher order computational complexity and storage requirement the key contribution of the proposed approach is that the information propagation on tpg can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph we prove that a graph diffusion process on tpg is equivalent to a novel iterative algorithm on the original graph which is guaranteed to converge after its convergence we obtain new edge weights that can be interpreted as new learned affinities we stress that the affinities are learned in an unsupervised setting we illustrate the benefits of the proposed approach for data manifolds composed of shapes images and image patches on two very different tasks of image retrieval and image segmentation with learned affinities we achieve the bull s eye retrieval score of 99 99 percent on the mpeg 7 shape dataset which is much higher than the state of the art algorithms when the data points are image patches the ncut with the learned affinities not only significantly outperforms the ncut with the original affinities but it also outperforms state of the art image segmentation methods

color constancy for multiple light sources color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes however in reality this assumption is often violated due to the presence of multiple light sources in this paper we will address more realistic scenarios where the uniform light source assumption is too restrictive first a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches rather than globally to the entire image after local patch based illuminant estimation these estimates are combined into more robust estimations and a local correction is applied based on a modified diagonal model quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene if the chromatic difference between these two illuminants is more than 1 the proposed framework outperforms algorithms based on the uniform light source assumption with error reduction up to approximately 30 otherwise when the chromatic difference is less than 1 and the scene can be considered to contain one approximately uniform light source the performance of the proposed method framework is similar to global color constancy methods