Semantic interactive image retrieval combining visual and conceptual content description

We address the challenge of semantic gap reduction for image retrieval through an improved support vector machines (SVM)-based active relevance feedback framework, together with a hybrid visual and conceptual content representation and retrieval. We introduce a new feature vector based on projecting the keywords associated to an image on a set of “key concepts” with the help of an external lexical database. We then put forward two improvements of SVM-based relevance feedback method. First, to optimize the transfer of information between the user and the system, we introduce a new active learning selection criterion that minimizes redundancy between the candidate images shown to the user. Second, as most image classes span a wide range of scales in the description space, we argue that the insensitivity of the SVM to the scale of the data is desirable in this context and we show how to obtain it by using specific kernel functions. Experimental evaluations show that the joint use of the new concept-based feature vector and the visual features with our relevance feedback scheme can significantly improve the quality of the results.