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Boosting multi-kernel locality-sensitive hashing for scalable image retrieval.
| Content Provider | CiteSeerX |
|---|---|
| Author | Xia, Hao Wu, Pengcheng Hoi, Steven C. H. Jin, Rong |
| Abstract | 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 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multi-kernel Locality-sensitive Hashing Scalable Image Retrieval High-dimensional Space Similarity Search Multiple Resource Multiple Kernel Several Different Form Extensive Experiment Kernel Similarity Single Kernel Key Challenge Retrieval Efficacy Multimedia Retrieval Application Kernelized Locality-sensitive Hashing Locality-sensitive Hashing Contentbased Multimedia Retrieval Various Algorithm Retrieval Performance State-of-the-art Technique Real-world Multimedia Application Large-scale Content-based Image Retrieval Novel Boosting Multi-kernel Locality-sensitive Hashing Regular Klsh |
| Content Type | Text |
