Boosting multi-kernel Locality-Sensitive Hashing for scalable image retrieval

• Main Authors: XIA, Hao, HOI, Steven C. H., WU, Pengcheng, JIN, Rong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: high-dimensional indexing; image retrieval; kernel methods; locality-sensitive hashing; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2343; https://ink.library.smu.edu.sg/context/sis_research/article/3343/viewcontent/Boosting_Multi_Kernel_Locality_Sensitive_Hashing_for_Scalable_Image_Retrieval.pdf
• Institution: Singapore Management University

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