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 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.

@InProceedings{KAVJ10,
  author       = "Josip Krapac and Moray Allan and Jakob Verbeek and Fr\'ed\'eric Jurie",
  title        = "Improving web-image search results using query-relative classi\fiers",
  booktitle    = "IEEE Conference on Computer Vision \& Pattern Recognition",
  pages        = "1094--1101",
  month        = "jun",
  year         = "2010",
  url          = "http://lear.inrialpes.fr/pubs/2010/KAVJ10"
}