Video Indexing/Retrieving Based On Video Object Abstraction and Temporal Modeling

摘要

In this paper, we present a novel scheme for object-based video indexing and retrieval based on video abstraction and semantic event modeling. The proposed algorithm consists of three major steps; Video Object (VO) extraction, object-based video abstraction and statistical modeling of semantic features. A video object abstraction algorithm based on clustering analysis is described for reducing data redundancy and providing reliable feature data for next stage of the algorithm. Semantic feature modeling scheme is also proposed, which is based on temporal variation of low-level features in object area between adjacent frames of video sequence. Each semantic feature is represented by a Hidden Markov Model (HMM) which characterizes the temporal nature of VO with various combinations of object features. We also include experimental results to demonstrate the effective performance of the proposed approach.