Image Retrieval using Long-Term Semantic Learning

The automatic computation of features for content-based image retrieval still has difficulties to represent the concepts the user has in mind. Whenever an additional learning strategy (such as relevance feedback) can improve the results of the search, the system performances still depend on the representation of the image collection. We introduce in this paper a supervised optimization of a set of feature vectors. According to an incomplete set of partial labels, the method improves the representation of the image collection, even if the size, the number, and the structure of the concepts are unknown. Experiments have been carried out on a large general database in order to validate our approach.