Improving web-image search results using query-relative classifiers

Josip Krapac (1) , Moray Allan (2) , Jakob Verbeek (1) , Frédéric Jurie (3)

1 LEAR - Learning and recognition in vision
2 GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
3 Equipe Image - Laboratoire GREYC - UMR6072

Web image search using text queries has received considerable attention. However, current state-of-the-art approaches require training models for every new query, and are therefore unsuitable for real-world web search applications. The key contribution of this paper is to introduce generic classifiers that are based on query-relative features which can be used for new queries without additional training. They combine textual features, based on the occurence of query terms in web pages and image meta-data, and visual histogram representations of images. The second contribution of the paper is a new database for the evaluation of web image search algorithms. It includes 71478 images returned by a web search engine for 353 different search queries, along with their meta-data and ground-truth annotations. Using this data set, we compared the image ranking performance of our model with that of the search engine, and with an approach that learns a separate classifier for each query. Our generic models that use query-relative features improve significantly over the raw search engine ranking, and also outperform the query-specific models.

Mots clés: image classification; query processing; search engines

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Josip Krapac, Moray Allan, Jakob Verbeek, Frédéric Jurie. Improving web-image search results using query-relative classifiers. CVPR 2010 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2010, San Francisco, United States. pp.1094-1101, ⟨10.1109/CVPR.2010.5540092⟩. ⟨inria-00548636⟩