A content-based image retrieval system based on unsupervised topological learning

Internet offers to its users an ever-increasing number of information. Among those, the multimodal data (images, text, video, sound) are widely requested by users, and there is a strong need for effective ways to process and to manage it, respectively. Most of existed algorithms/frameworks are doing only images annotations and the search is doing by this annotations, or combined with some clustering results, but most of them do not allow a quick browsing of these images. Even if the search is very quickly, but if the number of images is very large, the system must give the possibility to the user to browse this data. In this paper, an image retrieval system is presented, including detailed descriptions of used lwo-SOM (local weighting observations Self-Organizing Map) approach and a new interactive learning process using user information/response. Also, we show the use of unsupervised learning on an images dataset, we do not dispose of the labels, and we will not take into account the corresponding text for the images. The used real dataset contains 17812 images extracted from wikipedia pages, each of which is characterized by its color and texture.