Octopus: Aggressive search of multi-modality data using multifaceted knowledge base

An important trend in Web information processing is the support of multimedia retrieval. However, the most prevailing paradigm for multimedia retrieval, content-based retrieval (CBR), is a rather conservative one whose performance depends on a set of specifically defined low-level features and a carefully chosen sample object. In this paper, an aggressive search mechanism called Octopus is proposed which addresses the retrieval of multi-modality data using multifaceted knowledge. In particular, Octopus promotes a novel scenario in which the user supplies seed objects of arbitrary modality as the hint of his information need, and receives a set of multi-modality objects satisfying his need. The foundation of Octopus is a multifaceted knowledge base constructed on a layered graph model (LGM), which describes the relevance between media objects from various perspectives. Link analysis based retrieval algorithm is proposed based on the LGM. A unique relevance feedback technique is developed to update the knowledge base by learning from user behaviors, and to enhance the retrieval performance in a progressive manner. A prototype implementing the proposed approach has been developed to demonstrate its feasibility and capability through illustrative examples.