Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval

International audienceThis paper presents a search engine architecture, RETIN, aiming at retrieving com- plex categories in large image databases. For indexing, a scheme based on a two-step quantization process is presented to compute visual codebooks. The similarity be- tween images is represented in a kernel framework. Such a similarity is combined with online learning strategies motivated by recent Machine-Learning developments such as Active Learning. Additionally, an offine supervised learning is embedded in the kernel framework, offering a real opportunity to learn semantic categories. Experiments with real scenario carried out from the Corel Photo database demon- strate the effciency and the relevance of the RETIN strategy and its outstanding performances in comparison to up-to-date strategies