Content-based comparison of image collections via distance measuring of self-organised maps

Content-based image retrieval techniques have been under intensively research, mainly on extracting effective low level visual features for indexing and enabling fast query of individual images by feature matching over the indexing structure. In this paper, we propose to extend the content-based approach towards the problem of multimedia collection profiling and comparison. Our method is to carry out visual feature clustering using self-organised maps, and then apply distance measures on the generated feature maps to evaluate their similarity. A modified Hausdorff distance is defined over the feature maps and further verified in an experiment using four image collections. Some preliminary results are presented with a comparison of different distance measures obtained from profiles generated by two feature schemes.