Online multi-modal distance learning for scalable multimedia retrieval

  • Authors:
  • Hao Xia;Pengcheng Wu;Steven C.H. Hoi

  • Affiliations:
  • Nanyang Technological University, Singapore, Singapore;Nanyang Technological University, Singapore, Singapore;Nanyang Technological University, Singapore, Singapore

  • Venue:
  • Proceedings of the sixth ACM international conference on Web search and data mining
  • Year:
  • 2013

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Abstract

In many real-word scenarios, e.g., multimedia applications, data often originates from multiple heterogeneous sources or are represented by diverse types of representation, which is often referred to as "multi-modal data". The definition of distance between any two objects/items on multi-modal data is a key challenge encountered by many real-world applications, including multimedia retrieval. In this paper, we present a novel online learning framework for learning distance functions on multi-modal data through the combination of multiple kernels. In order to attack large-scale multimedia applications, we propose Online Multi-modal Distance Learning (OMDL) algorithms, which are significantly more efficient and scalable than the state-of-the-art techniques. We conducted an extensive set of experiments on multi-modal image retrieval applications, in which encouraging results validate the efficacy of the proposed technique.