Learning sparse latent representation and distance metric for image retrieval
The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of inter...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/19160 |
| _version_ | 1848749953575288832 |
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| author | Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. |
| author_facet | Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. |
| author_sort | Nguyen, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG. |
| first_indexed | 2025-11-14T07:29:08Z |
| format | Conference Paper |
| id | curtin-20.500.11937-19160 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:29:08Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-191602017-09-13T15:42:22Z Learning sparse latent representation and distance metric for image retrieval Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG. 2013 Conference Paper http://hdl.handle.net/20.500.11937/19160 10.1109/ICME.2013.6607435 restricted |
| spellingShingle | Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. Learning sparse latent representation and distance metric for image retrieval |
| title | Learning sparse latent representation and distance metric for image retrieval |
| title_full | Learning sparse latent representation and distance metric for image retrieval |
| title_fullStr | Learning sparse latent representation and distance metric for image retrieval |
| title_full_unstemmed | Learning sparse latent representation and distance metric for image retrieval |
| title_short | Learning sparse latent representation and distance metric for image retrieval |
| title_sort | learning sparse latent representation and distance metric for image retrieval |
| url | http://hdl.handle.net/20.500.11937/19160 |