Octopus

Abstract

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 multimodality 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.

Combining heterogeneous sources in an interactive multimedia content retrieval model

“…After reviewing the main related work, we now introduced our research in this paper, which is based on various approaches. The management of a multimodal query is based on the approaches of Yang et al (2002) and Marchand-Maillet et al (2011). The engine selection (handler) strategy is based on a mixture of the approaches in Renaud and Azzopardi (2012) and Demner-Fushman et al (2012), which employ the content of the query (terms) to analyse which RE to query.…”