logo Idiap Research Institute        
 [BibTeX] [Marc21]
A Discriminative Kernel-based Model to Rank Images from Text Queries
Type of publication: Journal paper
Citation: grangier:2008:tpami
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Volume: X
Year: 2008
Crossref: grangier:2007:idiap-07-38:
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.
Userfields: numero={X}, page={XX--XX}, ipdmembership={Learning},
Projects Idiap
Authors Grangier, David
Bengio, Samy
Added by: [UNK]
Total mark: 0
  • grangier-tpami-2007.pdf
  • grangier-tpami-2007.ps.gz