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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lu, Xinyan Wu, Fei Yan, Shuicheng Zhuang, Yueting Zhang, Zhongfei Rui, Yong |
| 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 uni-directional ranking examples. |
| Starting Page | 877 |
| Ending Page | 886 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450324045 |
| DOI | 10.1145/2502081.2502097 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-21 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Bi-directional learning to rank Latent space embedding Cross-media representation |
| Content Type | Text |
| Resource Type | Article |
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