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270
Product quantization for nearest neighbor search
, 2010
"... This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace q ..."
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Cited by 221 (31 self)
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This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The Euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy outperforming three stateoftheart approaches. The scalability of our approach is validated on a dataset of two billion vectors.
Kernelized localitysensitive hashing for scalable image search
 IEEE International Conference on Computer Vision (ICCV
, 2009
"... Fast retrieval methods are critical for largescale and datadriven vision applications. Recent work has explored ways to embed highdimensional features or complex distance functions into a lowdimensional Hamming space where items can be efficiently searched. However, existing methods do not apply ..."
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Cited by 165 (5 self)
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Fast retrieval methods are critical for largescale and datadriven vision applications. Recent work has explored ways to embed highdimensional features or complex distance functions into a lowdimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for highdimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize localitysensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm’s sublinear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful imagebased kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several largescale datasets, and show that it enables accurate and fast performance for examplebased object classification, feature matching, and contentbased retrieval. 1.
Efficient Object Category Recognition Using
"... Abstract. We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories a ..."
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Cited by 118 (9 self)
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Abstract. We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories ’ semantic meanings. The advantage of this descriptor is that it allows objectcategory queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36 % versus 42%), but at orders of magnitude lower computational cost.
Learning to hash with binary reconstructive embeddings
 in Proc. NIPS, 2009
"... Fast retrieval methods are increasingly critical for many largescale analysis tasks, and there have been several recent methods that attempt to learn hash functions for fast and accurate nearest neighbor searches. In this paper, we develop an algorithm for learning hash functions based on explicitl ..."
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Cited by 110 (1 self)
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Fast retrieval methods are increasingly critical for many largescale analysis tasks, and there have been several recent methods that attempt to learn hash functions for fast and accurate nearest neighbor searches. In this paper, we develop an algorithm for learning hash functions based on explicitly minimizing the reconstruction error between the original distances and the Hamming distances of the corresponding binary embeddings. We develop a scalable coordinatedescent algorithm for our proposed hashing objective that is able to efficiently learn hash functions in a variety of settings. Unlike existing methods such as semantic hashing and spectral hashing, our method is easily kernelized and does not require restrictive assumptions about the underlying distribution of the data. We present results over several domains to demonstrate that our method outperforms existing stateoftheart techniques. 1
H (2010a) Largescale image retrieval with compressed Fisher vectors
 In: CVPR Perronnin F, Sánchez J, Liu Y (2010b) Largescale
"... The problem of largescale image search has been traditionally addressed with the bagofvisualwords (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is wellsuited to the retrieval problem: it describes an image by ..."
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Cited by 104 (8 self)
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The problem of largescale image search has been traditionally addressed with the bagofvisualwords (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is wellsuited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is highdimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to largescale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speedup the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach. 1.
Hashing with graphs
 In ICML
, 2011
"... Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases. However, learning short codes that yield good search performance is still a challenge. Moreover, in many cases realworld data lives on a lowdimensional manifold, which should be taken into account t ..."
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Cited by 103 (30 self)
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Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases. However, learning short codes that yield good search performance is still a challenge. Moreover, in many cases realworld data lives on a lowdimensional manifold, which should be taken into account to capture meaningful nearest neighbors. In this paper, we propose a novel graphbased hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes. To make such an approach computationally feasible, we utilize Anchor Graphs to obtain tractable lowrank adjacency matrices. Our formulation allows constant time hashingof a newdatapointbyextrapolatinggraphLaplacian eigenvectors to eigenfunctions. Finally, we describe a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy. Experimental comparison with the other stateoftheart methods on two large datasets demonstrates the efficacy of the proposed method. 1.
Compressed Histogram of Gradients: A LowBitrate Descriptor
 INT J COMPUT VIS
, 2011
"... Establishing visual correspondences is an essential component of many computer vision problems, which is often done with local featuredescriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile visual search applications. We propose a framework f ..."
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Cited by 97 (23 self)
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Establishing visual correspondences is an essential component of many computer vision problems, which is often done with local featuredescriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile visual search applications. We propose a framework for computing low bitrate feature descriptors with a 20 × reduction in bit rate compared to stateoftheart descriptors. The framework offers low complexity and has significant speedup in the matching stage. We show how to efficiently compute distances between descriptors in the compressed domain eliminating the need for decoding. We perform a comprehensive performance comparison with SIFT, SURF, BRIEF, MPEG7 image signatures and other low bitrate descriptors and show that our proposed CHoG descriptor outperforms existing schemes significantly over a wide range of bitrates. We implement the descriptor in a mobile image retrieval system and for a database of 1 million CD, DVD and book covers, we achieve 96 % retrieval accuracy using only 4 KB of data per query image.
BSupervised hashing with kernels
 in Proc. IEEE Conf. Comput. Vis. Pattern Recognit
, 2012
"... Recent years have witnessed the growing popularity of hashing in largescale vision problems. It has been shown that the hashing quality could be boosted by leveraging supervised information into hash function learning. However, the existing supervised methods either lack adequate performance or oft ..."
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Cited by 81 (24 self)
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Recent years have witnessed the growing popularity of hashing in largescale vision problems. It has been shown that the hashing quality could be boosted by leveraging supervised information into hash function learning. However, the existing supervised methods either lack adequate performance or often incur cumbersome model training. In this paper, we propose a novel kernelbased supervised hashing model which requires a limited amount of supervised information, i.e., similar and dissimilar data pairs, and a feasible training cost in achieving high quality hashing. The idea is to map the data to compact binary codes whose Hamming distances are minimized on similar pairs and simultaneously maximized on dissimilar pairs. Our approach is distinct from prior works by utilizing the equivalence between optimizing the code inner products and the Hamming distances. This enables us to sequentially and efficiently train the hash functions one bit at a time, yielding very short yet discriminative codes. We carry out extensive experiments on two image benchmarks with up to one million samples, demonstrating that our approach significantly outperforms the stateofthearts in searching both metric distance neighbors and semantically similar neighbors, with accuracy gains ranging from 13 % to 46%. 1.
LocalitySensitive Binary Codes from ShiftInvariant Kernels,” Advances in neural information processing systems
, 2009
"... This paper addresses the problem of designing binary codes for highdimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distributionfree encoding scheme based on random projections, such that the expected Hamming distance be ..."
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Cited by 81 (1 self)
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This paper addresses the problem of designing binary codes for highdimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distributionfree encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shiftinvariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent stateoftheart method, spectral hashing. 1