Multi-level diversification approach of semantic-based image retrieval results

With the increasing popularity of social photograph-sharing Web sites, a huge mass of digital images, associated with a set of tags voluntarily introduced by amateur photographers, is daily hosted and consequently, the Tag-based social Image Retrieval technique has been widely adopted. However, tag-based queries are often too ambiguous and abstract to be considered as an efficient solution for the retrieval of the most relevant images that meet the users’ needs. As an alternative, the Semantic-based social Image Retrieval technique has emerged for the purpose of retrieving the relevant images covering as much possible the topics that a given ambiguous query (q) may have. Actually, the diversification strategies are a great challenge for researchers. In this context, we jointly investigate two processes at the ambiguous query preprocessing and postprocessing levels. On the one hand, we propose a Tag-based Query Semantic Reformulation process, which aims at reformulating the tag-based users’ queries, according to multiple semantic facets of the different images’ views, by using a set of predefined ontological semantic rules. On the other hand, we propose a Multi-level Image Diversification process that can first perform a two-level-based image clustering offline, and second, filter and re-rank the image cluster retrieval results according to their pertinence versus the reformulated query online. The experimental results and statistical analysis performed on a collection of 25.000 socio-tagged images shared on Flickr demonstrate the effectiveness of the proposed technique, which is compared with the research technique based on one-level-based image clustering, tag-based image research technique and recent CBIR techniques.