Machine learning tools for content-based search in large multimedia databases

Summary form only given. The talk deals with a new paradigm for multimedia search based on content. We present an alternative approach to classical search engines for information retrieval which can be used for large and generic multimedia repositories. We introduce an incremental evolution scheme within a collective network of (evolutionary) binary classifier (CNBC) framework. The proposed framework addresses the problems of feature/class scalability and achieves high classification and content-based retrieval performances over dynamic image repositories. The secret behind the success of CNBC is a novel design to implement the backbone of CNBC, namely the binary classifier. This is a special neural network which is optimally designed using the recently developed evolutionary optimization algorithm called multi-dimensional particle swarm optimization.