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...

• Type: Journal
• Year: 2012
• Where: SIGIR
• Authors: Hao Xia, Pengcheng Wu, Steven C. H. Hoi, Rong Jin
• Keywords: Based Image Retrieval, Information Storage And Retrieval, Information Technology, Real World Multimedia, SIGIR 2012