Abstract
This paper presents a novel scheme of 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. Semantic feature modeling scheme 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. The experimental results demonstrate the effective performance of the proposed approach.
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Lee, K. (2005). Semantic Feature Extraction Based on Video Abstraction and Temporal Modeling. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_48
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DOI: https://doi.org/10.1007/11492429_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26153-7
Online ISBN: 978-3-540-32237-5
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