Content Meets Semantics: Smarter Exploration of Image Collections - Presentation of Relevant Use Cases

Current techniques for the management of image collections exploit either user-provided annotations or automatically-extracted visual features. Although effective, the approach based on annotations cannot be efficient since the manual process of data tagging prevents its scalability. On the other hand, the organization and search grounded on visual features, such as color and texture, is known to be a powerful (since it can be made fully automatic), yet imprecise, retrieval paradigm, because of the semantic gap problem. This position paper advocates the combination of visual content and semantics as a critical binomial for effectively and efficiently managing and browsing image databases satisfying users’ expectations in quickly locating images of interest.