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31
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 84 (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.
Isotropic hashing,”
 Advances in Neural Information Processing Systems,
, 2012
"... Abstract Most existing hashing methods adopt some projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit (zero or one) by thresholding. Typically, the variances of different projected dimensions ..."
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Cited by 29 (2 self)
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Abstract Most existing hashing methods adopt some projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit (zero or one) by thresholding. Typically, the variances of different projected dimensions are different for existing projection functions such as principal component analysis (PCA). Using the same number of bits for different projected dimensions is unreasonable because largervariance dimensions will carry more information. Although this viewpoint has been widely accepted by many researchers, it is still not verified by either theory or experiment because no methods have been proposed to find a projection with equal variances for different dimensions. In this paper, we propose a novel method, called isotropic hashing (IsoHash), to learn projection functions which can produce projected dimensions with isotropic variances (equal variances). Experimental results on real data sets show that IsoHash can outperform its counterpart with different variances for different dimensions, which verifies the viewpoint that projections with isotropic variances will be better than those with anisotropic variances.
Multidimensional Spectral Hashing
"... Abstract. With the growing availability of very large image databases, there has been a surge of interest in methods based on “semantic hashing”, i.e. compact binary codes of datapoints so that the Hamming distance between codewords correlates with similarity. In reviewing and comparing existing me ..."
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Cited by 29 (0 self)
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Abstract. With the growing availability of very large image databases, there has been a surge of interest in methods based on “semantic hashing”, i.e. compact binary codes of datapoints so that the Hamming distance between codewords correlates with similarity. In reviewing and comparing existing methods, we show that their relative performance can change drastically depending on the definition of groundtruth neighbors. Motivated by this finding, we propose a new formulation for learning binary codes which seeks to reconstruct the affinity between datapoints, rather than their distances. We show that this criterion is intractable to solve exactly, but a spectral relaxation gives an algorithm where the bits correspond to thresholded eigenvectors of the affinity matrix, and as the number of datapoints goes to infinity these eigenvectors converge to eigenfunctions of LaplaceBeltrami operators, similar to the recently proposed Spectral Hashing (SH) method. Unlike SH whose performance may degrade as the number of bits increases, the optimal code using our formulation is guaranteed to faithfully reproduce the affinities as the number of bits increases. We show that the number of eigenfunctions needed may increase exponentially with dimension, but introduce a “kernel trick ” to allow us to compute with an exponentially large number of bits but using only memory and computation that grows linearly with dimension. Experiments shows that MDSH outperforms the stateofthe art, especially in the challenging regime of small distance thresholds. 1
Scalable action recognition with a subspace forest
 in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR
, 2012
"... We present a novel structure, called a Subspace Forest, designed to provide an efficient approximate nearest neighbor query of subspaces represented as points on Grassmann manifolds. We apply this structure to action recognition by representing actions as subspaces spanning a sequence of thumbna ..."
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Cited by 14 (0 self)
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We present a novel structure, called a Subspace Forest, designed to provide an efficient approximate nearest neighbor query of subspaces represented as points on Grassmann manifolds. We apply this structure to action recognition by representing actions as subspaces spanning a sequence of thumbnail image tiles extracted from a tracked entity. The Subspace Forest lifts the concept of randomized decision forests from classifying vectors to classifying subspaces, and employs a splitting method that respects the underlying manifold geometry. The Subspace Forest is an inherently parallel structure and is highly scalable due to O(logN) recognition time complexity. Our experimental results demonstrate stateoftheart classification accuracies on the wellknown KTH Actions and UCF Sports benchmarks, and a competitive score on Cambridge Gestures. In addition to being both highly accurate and scalable, the Subspace Forest is built without supervision and requires no extensive validation stage for model selection. Conceptually, the Subspace Forest could be used anywhere settoset feature matching is desired. 1.
QueryAdaptive Image Search with Hash Codes
, 2010
"... Scalable image search based on visual similarity has been an active topic of research in recent years. Stateoftheart solutions often use hashing methods to embed highdimensional image features into Hamming space, where search can be performed in realtime based on Hamming distance of compact ha ..."
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Cited by 8 (2 self)
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Scalable image search based on visual similarity has been an active topic of research in recent years. Stateoftheart solutions often use hashing methods to embed highdimensional image features into Hamming space, where search can be performed in realtime based on Hamming distance of compact hash codes. Unlike traditional metrics (e.g., Euclidean) that offer continuous distances, the Hamming distances are discrete integer values. As a consequence, there are often a large number of images sharing equal Hamming distances to a query, which largely hurts search results where finegrained ranking is very important. This paper introduces an approach that enables queryadaptive ranking of the returned images with equal Hamming distances to the queries. This is achieved by firstly offline learning bitwise weights of the hash codes for a diverse set of predefined semantic concept classes. We formulate the weight learning process as a quadratic programming problem that minimizes intraclass distance while preserving interclass relationship captured by original raw image features. Queryadaptive weights are then computed online by evaluating the proximity between a query and the semantic concept classes. With the queryadaptive bitwise weights, returned images can be easily ordered by weighted Hamming distance at a finergrained hash code level rather than the original Hamming distance level. Experiments on a Flickr image dataset show clear improvements from our proposed approach.
Composite Quantization for Approximate Nearest Neighbor Search
"... This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the ..."
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Cited by 5 (4 self)
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This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the indices of the selected elements. To efficiently compute the approximate distance of a query to a database vector using the short code, we introduce an extra constraint, constant interdictionaryelementproduct, resulting in that approximating the distance only using the distance of the query to each selected element is enough for nearest neighbor search. Experimental comparisonwith stateoftheart algorithms over several benchmark datasets demonstrates the efficacy of the proposed approach. 1.
Active evaluation of classifiers on large datasets
 In ICDM (Runnerup for Best paper award
, 2012
"... Abstract—The goal of this work is to estimate the accuracy of a classifier on a large unlabeled dataset based on a small labeled set and a human labeler. We seek to estimate accuracy and select instances for labeling in a loop via a continuously refined stratified sampling strategy. For stratifying ..."
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Cited by 3 (1 self)
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Abstract—The goal of this work is to estimate the accuracy of a classifier on a large unlabeled dataset based on a small labeled set and a human labeler. We seek to estimate accuracy and select instances for labeling in a loop via a continuously refined stratified sampling strategy. For stratifying data we develop a novel strategy of learning r bit hash functions to preserve similarity in accuracy values. We show that our algorithm provides better accuracy estimates than existing methods for learning distance preserving hash functions. Experiments on a wide spectrum of real datasets show that our estimates achieve between 15 % and 62 % relative reduction in error compared to existing approaches. We show how to perform stratified sampling on unlabeled data that is so large that in an interactive setting even a single sequential scan is impractical. We present an optimal algorithm for performing importance sampling on a static index over the data that achieves close to exact estimates while reading three orders of magnitude less data. KeywordsAccuracy estimation, active evaluation, learning hash functions. I.
Complementary projection hashing
 in Proceedings of the IEEE International Conference on Computer Vision
, 2013
"... Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors search problem in many vision applications. Generally, these hashing approaches generate 2c buckets, where c is the length of the hash code. A good hashing method should satisfy the following two requi ..."
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Cited by 3 (0 self)
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Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors search problem in many vision applications. Generally, these hashing approaches generate 2c buckets, where c is the length of the hash code. A good hashing method should satisfy the following two requirements: 1) mapping the nearby data points into the same bucket or nearby (measured by the Hamming distance) buckets. 2) all the data points are evenly distributed among all the buckets. In this paper, we propose a novel algorithm named Complementary Projection Hashing (CPH) to find the optimal hashing functions which explicitly considers the above two requirements. Specifically, CPH aims at sequentially finding a series of hyperplanes (hashing functions) which cross the sparse region of the data. At the same time, the data points are evenly distributed in the hypercubes generated by these hyperplanes. The experiments comparing with the stateoftheart hashing methods demonstrate the effectiveness of the proposed method. 1.
Fast neighborhood graph search using cartesian concatenation
 In ICCV
"... In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors ..."
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Cited by 3 (3 self)
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In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors. Each bridge vector is connected with a few reference vectors near to it, forming a bridge graph. Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the bestfirst strategy. The success of our approach stems from two factors: the exact nearest neighbor search over a large number of bridge vectors can be done quickly, and the reference vectors connected to a bridge (reference) vector near the query are also likely to be near the query. Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms stateoftheart ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IVFADC system [18] also shows superior performance over the BIGANN dataset of 1 billion SIFT features compared with the best previously published result. 1.
LargeScale Machine Learning for Classification and Search
, 2012
"... With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing largescale machine learning techniques for t ..."
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Cited by 2 (1 self)
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With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing largescale machine learning techniques for the purpose of making classification and nearest neighbor search practical on gigantic databases. Our first approach is to explore data graphs to aid classification and nearest neighbor search. A graph offers an attractive way of representing data and discovering the essential information such as the neighborhood structure. However, both of the graph construction process and graphbased learning techniques become computationally prohibitive at a large scale. To this end, we present an efficient large graph construction approach and subsequently apply it to develop scalable semisupervised learning and unsupervised hashing algorithms. Our unique contributions on the graphrelated topics include: 1. Large Graph Construction: Conventional neighborhood graphs such as kNN graphs require a quadratic time complexity, which is inadequate for largescale applications mentioned above. To overcome this bottleneck, we present a novel graph construction approach,