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

This paper presents a search engine architecture, RETIN, aiming at retrieving complex categories in large image databases. For indexing, a scheme based on a two-step quantization process is presented to compute visual codebooks. The similarity between 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 offline 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 demonstrate the efficiency and the relevance of the RETIN strategy and its outstanding performances in comparison to up-to-date strategies.