Content-based comic retrieval using multilayer graph representation and frequent graph mining
Résumé
Comics has its large audience and market throughout the world, yet despite the huge research interest given to content-based image retrieval (CBIR) systems, the question of how to effectively retrieve comic images has been little studied. In this paper, we propose a scheme to represent and retrieve comic-page images using attributed Region Adjacency Graphs (RAGs) and their frequent subgraphs. We first extract the graphical structures and local features of each panel of the whole comic volume, then separate different categories of local features to different layers of attributed RAGs. After that, a list of frequent subgraphs for each layer is obtained by using frequent subgraph mining (FSM) technique. For indexing and CBIR purpose, the recognition and ranking are done by checking for isomorphism between the graphs representing the query versus the discovered frequent subgraphs. Our experimental results show that the proposed approach can achieve reliable retrieval results of comic images using query-by-example (QBE) model.