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

By Hao Xia, Pengcheng Wu, Steven C. H. Hoi, Rong Jin. 2012

40 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

  1. Jingkuan Song
  2. Heng Tao Shen
  3. Jingdong Wang
  4. Javen Qinfeng Shi
  5. Anton van den Hengel

In Computer Vision and Pattern Recognition

  1. Jingkuan Song
  2. Julian McAuley
  3. Jingdong Wang
  4. Heng Tao Shen
  5. Dacheng Tao
  6. Yi Yang
  7. Anton van den Hengel
  8. Luc Van Gool

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