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792
Learning globallyconsistent local distance functions for shapebased image retrieval and classification
 In ICCV
, 2007
"... We address the problem of visual category recognition by learning an imagetoimage distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patchbased feat ..."
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Cited by 150 (3 self)
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We address the problem of visual category recognition by learning an imagetoimage distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patchbased feature vectors common in object recognition work as a basis for our imagetoimage distance functions. Our largemargin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions— a different parameterized function for every image of our training set—whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark. Using fifteen training images per category, we achieved a mean recognition rate of 63.2 % and
Active SemiSupervision for Pairwise Constrained Clustering
 Proc. 4th SIAM Intl. Conf. on Data Mining (SDM2004
"... Semisupervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of mustlink and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for acti ..."
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Cited by 134 (9 self)
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Semisupervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of mustlink and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision. 1
Learning spectral clustering
, 2003
"... Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive a new cost fu ..."
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Cited by 120 (4 self)
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Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive a new cost function for spectral clustering based on a measure of error between a given partition and a solution of the spectral relaxation of a minimum normalized cut problem. Minimizing this cost function with respect to the partition leads to a new spectral clustering algorithm. Minimizing with respect to the similarity matrix leads to an algorithm for learning the similarity matrix. We develop a tractable approximation of our cost function that is based on the power method of computing eigenvectors. 1
Segmentation of multivariate mixed data via lossy coding and compression
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... Abstract—In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions, which are allowed to be almost degenerate. The goal is to find the optimal segmen ..."
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Cited by 108 (17 self)
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Abstract—In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions, which are allowed to be almost degenerate. The goal is to find the optimal segmentation that minimizes the overall coding length of the segmented data, subject to a given distortion. By analyzing the coding length/rate of mixed data, we formally establish some strong connections of data segmentation to many fundamental concepts in lossy data compression and ratedistortion theory. We show that a deterministic segmentation is approximately the (asymptotically) optimal solution for compressing mixed data. We propose a very simple and effective algorithm that depends on a single parameter, the allowable distortion. At any given distortion, the algorithm automatically determines the corresponding number and dimension of the groups and does not involve any parameter estimation. Simulation results reveal intriguing phasetransitionlike behaviors of the number of segments when changing the level of distortion or the amount of outliers. Finally, we demonstrate how this technique can be readily applied to segment real imagery and bioinformatic data. Index Terms—Multivariate mixed data, data segmentation, data clustering, rate distortion, lossy coding, lossy compression, image segmentation, microarray data clustering. 1
Image retrieval and classification using local distance functions
 Advances in Neural Information Processing Systems
, 2006
"... In this paper we introduce and experiment with a framework for learning local perceptual distance functions for visual recognition. We learn a distance function for each training image as a combination of elementary distances between patchbased visual features. We apply these combined local distanc ..."
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Cited by 108 (3 self)
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In this paper we introduce and experiment with a framework for learning local perceptual distance functions for visual recognition. We learn a distance function for each training image as a combination of elementary distances between patchbased visual features. We apply these combined local distance functions to the tasks of image retrieval and classification of novel images. On the Caltech 101 object recognition benchmark, we achieve 60.3 % mean recognition across classes using 15 training images per class, which is better than the best published performance by Zhang, et al. 1
Fast Image Search for Learned Metrics
"... We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the images’ underlying relationships well. To allow sublinear time similarity search unde ..."
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Cited by 104 (11 self)
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We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the images’ underlying relationships well. To allow sublinear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized localitysensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality make it infeasible to learn an explicit weighting over the feature dimensions. We demonstrate the approach applied to a variety of image datasets. Our learned metrics improve accuracy relative to commonlyused metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.
Semisupervised graph clustering: a kernel approach
, 2008
"... Semisupervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semisupervised clustering algorithms are designed for data represented as vectors. In this ..."
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Cited by 94 (3 self)
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Semisupervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semisupervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vectorbased and graphbased approaches. We first show that a recentlyproposed objective function for semisupervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel kmeans objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel kmeans and several graph clustering objectives enables us to perform semisupervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semisupervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with nonlinear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current stateoftheart semisupervised algorithms on both vectorbased and graphbased data sets.
Visual Rank: applying Page Rank to largescale image search
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2008
"... Abstract—Because of the relative ease in understanding and processing text, commercial imagesearch systems often rely on techniques that are largely indistinguishable from text search. Recently, academic studies have demonstrated the effectiveness of employing imagebased features to provide either ..."
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Cited by 92 (4 self)
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Abstract—Because of the relative ease in understanding and processing text, commercial imagesearch systems often rely on techniques that are largely indistinguishable from text search. Recently, academic studies have demonstrated the effectiveness of employing imagebased features to provide either alternative or additional signals to use in this process. However, it remains uncertain whether such techniques will generalize to a large number of popular Web queries and whether the potential improvement to search quality warrants the additional computational cost. In this work, we cast the imageranking problem into the task of identifying “authority ” nodes on an inferred visual similarity graph and propose VisualRank to analyze the visual link structures among images. The images found to be “authorities ” are chosen as those that answer the imagequeries well. To understand the performance of such an approach in a real system, we conducted a series of largescale experiments based on the task of retrieving images for 2,000 of the most popular products queries. Our experimental results show significant improvement, in terms of user satisfaction and relevancy, in comparison to the most recent Google Image Search results. Maintaining modest computational cost is vital to ensuring that this procedure can be used in practice; we describe the techniques required to make this system practical for largescale deployment in commercial search engines. Index Terms—Image ranking, contentbased image retrieval, eigenvector centrality, graph theory. Ç
NonRedundant Data Clustering
, 2004
"... Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to de ..."
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Cited by 90 (3 self)
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Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and nonnumeric attributes. We present experimental results for applications in text mining and computer vision.