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Title: Cross-media semantic representation via bi-directional learning to rank
Authors: Wu, F.
Lu, X.
Zhang, Z.
Yan, S. 
Rui, Y.
Zhuang, Y.
Keywords: Bi-directional learning to rank
Cross-media representation
Latent space embedding
Issue Date: 2013
Source: Wu, F.,Lu, X.,Zhang, Z.,Yan, S.,Rui, Y.,Zhuang, Y. (2013). Cross-media semantic representation via bi-directional learning to rank. MM 2013 - Proceedings of the 2013 ACM Multimedia Conference : 877-886. ScholarBank@NUS Repository.
Abstract: In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. Existing approaches take either one-to-one paired data or uni-directional ranking examples (i.e., utilizing only text-query-image ranking examples or image-query text ranking examples) as training examples, which do not make full use of bi-directional ranking examples (bi-directional ranking means that both text-query-image and image-query text ranking examples are utilized in the training period) to achieve a better performance. In this paper, we consider learning a cross-media representation model from the perspective of optimizing a listwise ranking problem while taking advantage of bi-directional ranking examples. We propose a general cross-media ranking algorithm to optimize the bi-directional listwise ranking loss with a latent space embedding, which we call Bi-directional Cross-Media Semantic Representation Model (Bi-CMSRM). The latent space embedding is discriminatively learned by the structural large margin learning for optimization with certain ranking criteria (mean average precision in this paper) directly. We evaluate Bi-CMSRM on the Wikipedia and NUS-WIDE datasets and show that the utilization of the bi-directional ranking examples achieves a much better performance than only using the unidirectional ranking examples. Copyright © 2013 ACM.
Source Title: MM 2013 - Proceedings of the 2013 ACM Multimedia Conference
ISBN: 9781450324045
DOI: 10.1145/2502081.2502097
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