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2012
ACM

Boosting multi-kernel locality-sensitive hashing for scalable image retrieval

7 years 8 months ago
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) is the most popular one, which recently has been extended to Kernelized Locality-Sensitive Hashing (KLSH) by exploiting kernel similarity for better retrieval efficacy. Typically, KLSH works only with a single kernel, which is often limited in real-world multimedia applications, where data may originate from multiple resources or can be represented in several different forms. For example, in contentbased multimedia retrieval, a variety of features can be extracted to represent contents of an image. To overcome the limitation of regular KLSH, we propose a novel Boosting Multi-Kernel LocalitySensitive Hashing (BMKLSH) scheme that significantly boosts the retrieval performance of KLSH by making use of multiple kernels. We conduct extensi...
Hao Xia, Pengcheng Wu, Steven C. H. Hoi, Rong Jin
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where SIGIR
Authors Hao Xia, Pengcheng Wu, Steven C. H. Hoi, Rong Jin
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