Knowledge-Based Image Processing for Classification and Recognition in Surveillance Applications

摘要

This short paper serves as preface for the ICIP special session on knowledge-based image processing for classification in surveillance applications. This special session presents work on integrative research aimed at automatic classification and semantic-based recognition of scenes and events for surveillance applications. The focus is integrative research on low-level multimedia analysis, knowledge extraction and semantic analysis. The eight papers selected for the special session target convergence of these fields by integrating, for a purpose, what can be disparate disciplines. The integration covers image processing, knowledge representation, information retrieval and semantic analysis. The targeted application scenario involves the processing of input data captured by video-cameras where security is of interest. The work presented in this special session has originated in two large international cooperative projects funded by the European commission under the sixth framework programme of the Information Society Technology (1ST). The mandate and scope of these two projects is very generic embracing several applications that rely on technology for bridging the gap between low-level content descriptions that can be computed automatically by a machine and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media (i.e. the semantic gap). However, the section presented in these conference proceedings is restricted to research work undertaken under more limited scenarios for specific video-based surveillance