Conference Proceeding Article
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 content-based 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 Locality-Sensitive Hashing (BMKLSH) scheme that significantly boosts the retrieval performance of KLSH by making use of multiple kernels. We conduct extensive experiments for large-scale content-based image retrieval, in which encouraging results show that the proposed method outperforms the state-of-the-art techniques
Computer Sciences | Databases and Information Systems
Data Management and Analytics
SIGIR'12: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval: August 12-16, 2012, Portland, Oregon
City or Country
XIA, Hao; HOI, Steven C. H.; WU, Pengcheng; and JIN, Rong.
Boosting Multi-Kernel Locality-Sensitive Hashing for Scalable Image Retrieval. (2012). SIGIR'12: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval: August 12-16, 2012, Portland, Oregon. 55-64. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2343