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15
Fast supervised hashing with decision trees for highdimensional data
 In Proc. CVPR
, 2014
"... Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Nonlinear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel ..."
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Cited by 14 (3 self)
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Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Nonlinear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve nonlinearity in hashing, which achieve encouraging retrieval performance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving nonlinearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data. In our approach, we first propose submodular formulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving largescale inference. Then we learn hash functions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most stateoftheart methods in retrieval precision and training time. Especially for highdimensional data, our method is orders of magnitude faster than many methods in terms of training time. 1.
A general twostep approach to learningbased hashing
 In Proc. Int. Conf. Comp. Vis. (ICCV
"... Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are ..."
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Cited by 12 (4 self)
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Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problemspecific hashing methods. Our framework decomposes hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the stateoftheart. 1
Discrete Graph Hashing
"... Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases. In particular, learning based hashing has received considerable attention due to its appealing storage and search efficiency. However, the performance of most unsupervised learning based hashing meth ..."
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Cited by 5 (2 self)
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Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases. In particular, learning based hashing has received considerable attention due to its appealing storage and search efficiency. However, the performance of most unsupervised learning based hashing methods deteriorates rapidly as the hash code length increases. We argue that the degraded performance is due to inferior optimization procedures used to achieve discrete binary codes. This paper presents a graphbased unsupervised hashing model to preserve the neighborhood structure of massive data in a discrete code space. We cast the graph hashing problem into a discrete optimization framework which directly learns the binary codes. A tractable alternating maximization algorithm is then proposed to explicitly deal with the discrete constraints, yielding highquality codes to well capture the local neighborhoods. Extensive experiments performed on four large datasets with up to one million samples show that our discrete optimization based graph hashing method obtains superior search accuracy over stateoftheart unsupervised hashing methods, especially for longer codes. 1
Supervised discrete hashing
 In Proc. CVPR
, 2015
"... Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for highdimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints ..."
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Cited by 4 (1 self)
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Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for highdimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NPhard in general). In this work, we propose a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm. One of the key steps in this algorithm is to solve a regularization subproblem associated with the NPhard binary optimization. We show that the subproblem admits an analytical solution via cyclic coordinate descent. As such, a highquality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the stateoftheart hashing methods in largescale image retrieval. 1.
Locally Linear Hashing for Extracting NonLinear Manifolds
"... Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data. In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming spac ..."
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Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data. In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming space, which can be learned by localitysensitive sparse coding. We cast the problem as a joint minimization of reconstruction error and quantization loss, and show that, despite its NPhardness, a local optimum can be obtained efficiently via alternative optimization. Our method distinguishes itself from existing methods in its remarkable ability to extract the nearest neighbors of the query from the same manifold, instead of from the ambient space. On extensive experiments on various image benchmarks, our results improve previous stateoftheart by 2874 % typically, and 627 % on the Yale face data. 1.
Hashing for Similarity Search: A Survey
, 2014
"... Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this pap ..."
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Cited by 2 (2 self)
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Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space.
Optimizing Ranking Measures for Compact Binary Code Learning
"... Abstract. Hashing has proven a valuable tool for largescale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest ..."
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Abstract. Hashing has proven a valuable tool for largescale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest—multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cuttingplane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few stateoftheart hashing methods. 1
Graph cuts for supervised binary coding
 In Proc. ECCV
, 2014
"... Abstract. Learning short binary codes is challenged by the inherent discrete nature of the problem. The graph cuts algorithm is a wellstudied discrete label assignment solution in computer vision, but has not yet been applied to solve the binary coding problems. This is partially because it was unc ..."
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Abstract. Learning short binary codes is challenged by the inherent discrete nature of the problem. The graph cuts algorithm is a wellstudied discrete label assignment solution in computer vision, but has not yet been applied to solve the binary coding problems. This is partially because it was unclear how to use it to learn the encoding (hashing) functions for outofsample generalization. In this paper, we formulate supervised binary coding as a single optimization problem that involves both the encoding functions and the binary label assignment. Then we apply the graph cuts algorithm to address the discrete optimization problem involved, with no continuous relaxation. This method, named as Graph Cuts Coding (GCC), shows competitive results in various datasets.
191 PUBLICATIONS 2,525 CITATIONS SEE PROFILE
, 2014
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.