A Discriminative Kernel-Based Approach to Rank Images from Text Queries

@article{citeulike:3190425, abstract = {This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The proposed model hence addresses the retrieval problem directly and does not rely on an intermediate image annotation task, which contrasts with previous research. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. The experiments performed over stock photography data show the advantage of our discriminative ranking approach over state-of-the-art alternatives (e.g. our model yields 26.3\% average precision over the Corel dataset, which should be compared to 22.0\%, for the best alternative model evaluated). Further analysis of the results shows that our model is especially advantageous over difficult queries such as queries with few relevant pictures or multiple-word queries.}, author = {Grangier, D. and Bengio, S. }, booktitle = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, citeulike-article-id = {3190425}, doi = {http://dx.doi.org/10.1109/TPAMI.2007.70791}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, keywords = {discriminative, image, kernel, retrieval, text}, number = {8}, pages = {1371--1384}, posted-at = {2008-09-04 08:12:44}, priority = {4}, title = {A Discriminative Kernel-Based Approach to Rank Images from Text Queries}, url = {http://dx.doi.org/10.1109/TPAMI.2007.70791}, volume = {30}, year = {2008} }

TY - JOUR ID - citeulike:3190425 L3 - citeulike-article-id:3190425 N2 - This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The proposed model hence addresses the retrieval problem directly and does not rely on an intermediate image annotation task, which contrasts with previous research. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. The experiments performed over stock photography data show the advantage of our discriminative ranking approach over state-of-the-art alternatives (e.g. our model yields 26.3% average precision over the Corel dataset, which should be compared to 22.0%, for the best alternative model evaluated). Further analysis of the results shows that our model is especially advantageous over difficult queries such as queries with few relevant pictures or multiple-word queries. IS - 8 JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on EP - 1384 TI - A Discriminative Kernel-Based Approach to Rank Images from Text Queries VL - 30 T2 - Pattern Analysis and Machine Intelligence, IEEE Transactions on SP - 1371 KW - discriminative KW - image KW - kernel KW - retrieval KW - text AU - Grangier, D AU - Bengio, S PY - 2008/// UR - http://dx.doi.org/10.1109/TPAMI.2007.70791 ER -