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45
Large Scale Online Learning of Image Similarity through Ranking
"... Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, user ..."
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Cited by 79 (3 self)
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Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, the approaches that exist today for learning such semantic similarity do not scale to large datasets. This is both because typically their CPU and storage requirements grow quadratically with the sample size, and because many methods impose complex positivity constraints on the space of learned similarity functions. The current paper presents OASIS, an Online Algorithm for Scalable Image Similarity learning that learns a bilinear similarity measure over sparse representations. OASIS is an online dual approach using the passiveaggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost. Our experiments show that OASIS is both fast and accurate at a wide range of scales: for a dataset with thousands of images, it achieves better results than existing stateoftheart methods, while being an order of
X.: Fusing robust face region descriptors via multiple metric learning for face recognition
 in the wild. In: Computer Vision and Pattern Recognition (CVPR), IEEE
, 2013
"... In many realworld face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or lowquality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into ..."
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Cited by 35 (4 self)
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In many realworld face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or lowquality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatialtemporal volumes) and then represent each block (resp. volume) by sumpooling the nonnegative sparse codes of positionfree patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. SpatialTemporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwiseconstrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the stateoftheart performances on two realworld datasets LFW and YouTube Faces (YTF) according to the restricted protocol. 1.
Metric and Kernel Learning Using a Linear Transformation
"... Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over lowdimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new ..."
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Cited by 30 (2 self)
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Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over lowdimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new data points. In this paper, we study the connections between metric learning and kernel learning that arise when studying metric learning as a linear transformation learning problem. In particular, we propose a general optimization framework for learning metrics via linear transformations, and analyze in detail a special case of our framework—that of minimizing the LogDet divergence subject to linear constraints. We then propose a general regularized framework for learning a kernel matrix, and show it to be equivalent to our metric learning framework. Our theoretical connections between metric and kernel learning have two main consequences: 1) the learned kernel matrix parameterizes a linear transformation kernel function and can be applied inductively to new data points, 2) our result yields a constructive method for kernelizing most existing Mahalanobis metric learning formulations. We demonstrate our learning approach by applying it to largescale real world problems in computer vision, text mining and semisupervised kernel dimensionality reduction. Keywords: divergence metric learning, kernel learning, linear transformation, matrix divergences, logdet 1.
X.: Locally aligned feature transforms across views
 In: IEEE International Conference on Computer Vision and Pattern Recognition
, 2013
"... In this paper, we propose a new approach for matching images observed in different camera views with complex crossview transforms and apply it to person reidentification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross ..."
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Cited by 30 (2 self)
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In this paper, we propose a new approach for matching images observed in different camera views with complex crossview transforms and apply it to person reidentification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of crossview transforms. The visual features of an image pair from different views are first locally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering crossview transforms. They are jointly learned by utilizing sparsityinducing norm and information theoretical regularization. This approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the stateoftheart metric learning and person reidentification methods show the superior performance of our approach. 1.
An Online Algorithm for Large Scale Image Similarity Learning
"... Learning a measure of similarity between pairs of objects is a fundamental problem in machine learning. It stands in the core of classification methods like kernel machines, and is particularly useful for applications like searching for images that are similar to a given image or finding videos that ..."
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Cited by 28 (0 self)
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Learning a measure of similarity between pairs of objects is a fundamental problem in machine learning. It stands in the core of classification methods like kernel machines, and is particularly useful for applications like searching for images that are similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, current approaches for learning similarity do not scale to large datasets, especially when imposing metric constraints on the learned similarity. We describe OASIS, a method for learning pairwise similarity that is fast and scales linearly with the number of objects and the number of nonzero features. Scalability is achieved through online learning of a bilinear model over sparse representations using a large margin criterion and an efficient hinge loss cost. OASIS is accurate at a wide range of scales: on a standard benchmark with thousands of images, it is more precise than stateoftheart methods, and faster by orders of magnitude. On 2.7 million images collected from the web, OASIS can be trained within 3 days on a single CPU. The nonmetric similarities learned by OASIS can be transformed into metric similarities, achieving higher precisions than similarities that are learned as metrics in the first place. This suggests an approach for learning a metric from data that is larger by orders of magnitude than was handled before. 1
Regression on fixedrank positive semidefinite matrices: a Riemannian approach
 JMLR
"... The paper addresses the problem of learning a regression model parameterized by a fixedrank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to highdimensional problems. The mathematical developments rely on the theory of gradient descent al ..."
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Cited by 18 (7 self)
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The paper addresses the problem of learning a regression model parameterized by a fixedrank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to highdimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks.
Efficient Similarity Search for Covariance Matrices via the JensenBregman LogDet Divergence
"... Covariance matrices provide compact, informative feature descriptors for use in several computer vision applications, such as peopleappearance tracking, diffusiontensor imaging, activity recognition, among others. A key task in many of these applications is to compare different covariance matrices ..."
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Cited by 11 (3 self)
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Covariance matrices provide compact, informative feature descriptors for use in several computer vision applications, such as peopleappearance tracking, diffusiontensor imaging, activity recognition, among others. A key task in many of these applications is to compare different covariance matrices using a (dis)similarity function. A natural choice here is the Riemannian metric corresponding to the manifold inhabited by covariance matrices. But computations involving this metric are expensive, especially for large matrices and even more so, in gradientbased algorithms. To alleviate these difficulties, we advocate a novel dissimilarity measure for covariance matrices: the JensenBregman LogDet Divergence. This divergence enjoys several useful theoretical properties, but its greatest benefits are: (i) lower computational costs (compared to standard approaches); and (ii) amenability for use in nearestneighbor retrieval. We show numerous experiments to substantiate these claims.
Online Learning in the Manifold of LowRank Matrices
"... When learning models that are represented in matrix forms, enforcing a lowrank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches for minimizing functions over the set of lowrank matrices are eith ..."
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Cited by 9 (0 self)
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When learning models that are represented in matrix forms, enforcing a lowrank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches for minimizing functions over the set of lowrank matrices are either prohibitively time consuming (repeated singular value decomposition of the matrix) or numerically unstable (optimizing a factored representation of the low rank matrix). We build on recent advances in optimization over manifolds, and describe an iterative online learning procedure, consisting of a gradient step, followed by a secondorder retraction back to the manifold. While the ideal retraction is hard to compute, and so is the projection operator that approximates it, we describe another secondorder retraction that can be computed efficiently, with run time and memory complexity of O ((n + m)k) for a rankk matrix of dimension m × n, given rankone gradients. We use this algorithm, LORETA, to learn a matrixform similarity measure over pairs of documents represented as high dimensional vectors. LORETA improves the mean average precision over a passive aggressive approach in a factorized model, and also improves over a full model trained over preselected features using the same memory requirements. LORETA also showed consistent improvement over standard methods in a large (1600 classes) multilabel image classification task. 1
JensenBregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices
, 2012
"... Covariance matrices have found success in several computer vision applications, including activity recognition, visual surveillance, and diffusion tensor imaging. This is because they provide an easy platform for fusing multiple features compactly. An important task in all of these applications is t ..."
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Cited by 8 (2 self)
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Covariance matrices have found success in several computer vision applications, including activity recognition, visual surveillance, and diffusion tensor imaging. This is because they provide an easy platform for fusing multiple features compactly. An important task in all of these applications is to compare two covariance matrices using a (dis)similarity function, for which the common choice is the Riemannian metric on the manifold inhabited by these matrices. As this Riemannian manifold is not flat, the dissimilarities should take into account the curvature of the manifold. As a result such distance computations tend to slow down, especially when the matrix dimensions are large or gradients are required. Further, suitability of the metric to enable efficient nearest neighbor retrieval is an important requirement in the contemporary times of big data analytics. To alleviate these difficulties, this paper proposes a novel dissimilarity measure for covariances, the JensenBregman LogDet Divergence (JBLD). This divergence enjoys several desirable theoretical properties, at the same time is computationally less demanding (compared to standard measures). Utilizing the fact that the squareroot of JBLD is a metric, we address the problem of efficient nearest neighbor retrieval on large covariance datasets via a metric tree data structure. To this end, we propose a KMeans clustering algorithm on JBLD. We demonstrate the superior performance of JBLD on covariance datasets from several computer vision applications.