Multiple feature kernel hashing for large-scale visual search
Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and oth...
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| Published in: | Pattern recognition Vol. 47; no. 2; pp. 748 - 757 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.02.2014
Elsevier |
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.
•We propose a generic multiple feature hashing framework using multiple kernels.•Visual features are implicitly mapped and concatenated to reduce complexity.•We formulate both supervised and unsupervised hashing problems in the framework.•Alternating optimization ways efficiently learn hashing functions and the kernel space.•Experiments validate the superior performances and efficiency of the proposed approach. |
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| AbstractList | Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.
•We propose a generic multiple feature hashing framework using multiple kernels.•Visual features are implicitly mapped and concatenated to reduce complexity.•We formulate both supervised and unsupervised hashing problems in the framework.•Alternating optimization ways efficiently learn hashing functions and the kernel space.•Experiments validate the superior performances and efficiency of the proposed approach. Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. |
| Author | He, Junfeng Lang, Bo Liu, Xianglong |
| Author_xml | – sequence: 1 givenname: Xianglong surname: Liu fullname: Liu, Xianglong email: xlliu@nlsde.buaa.edu.cn, xlliuchina@gmail.com organization: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China – sequence: 2 givenname: Junfeng surname: He fullname: He, Junfeng organization: Department of Electrical Engineering, Columbia University, New York, NY 10027, USA – sequence: 3 givenname: Bo surname: Lang fullname: Lang, Bo organization: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China |
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| Keywords | Compact hashing Multiple features Locality-sensitive hashing Multiple kernels Performance evaluation Automatic classification State of the art Similarity Image retrieval Information retrieval Unsupervised classification Signal classification Optimization Kernel method Accuracy Algorithm performance Visual search Hashing |
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| SubjectTerms | Analogies Applied sciences Compact hashing Exact sciences and technology Image processing Information theory Information, signal and communications theory Kernels Locality-sensitive hashing Multiple features Multiple kernels Optimization Pattern recognition Preserves Searching Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise State of the art Telecommunications and information theory Visual |
| Title | Multiple feature kernel hashing for large-scale visual search |
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