Improving web-image search results using query-relative classifiers

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 occurrence 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 gen...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Josip Krapac, Moray Allan, Jakob Verbeek, Frédéric Jurie
• Keywords: CVPR 2010, Image Ranking, Image Retrieval