Automatic indexing of comic page images for query by example based focused content retrieval

Automatic indexing of comic-page-image reposito- ries has evolved as an interesting research problem for graphics recognition research community. In this paper we present a sys- tem for automatically indexing the comic-page-images in order to achieve query by example (QBE) based focused content retrieval. In our system we represent the comic-page-images by attributed graphs and translate the problem of automatic indexing / QBE based focused content retrieval as a subgraph spotting problem. Our system uses an explicit graph embedding technique to embed the comic-page-image graphs into numeric feature vectors and then employs state-of-the-art machine learning tools for automatic indexing / QBE based focused content retrieval. Experimental results are presented for automatic indexing and QBE based focused content retrieval in a comic-page-image repository.