Title

A Framework For Semantic Classification Of Scenes Using Finite State Machines

Abstract

We address the problem of classifying scenes from feature films into semantic categories and propose a robust framework for this problem. We propose that the Finite State Machines (FSM) are suitable for detecting and classifying scenes and demonstrate their usage for three types of movie scenes; conversation, suspense and action. Our framework utilizes the structural information of the scenes together with the low and mid-level features. Low level features of video including motion and audio energy and a mid-level feature, face detection, are used in our approach. The transitions of the FSMs are determined by the features of each shot in the scene. Our FSMs have been experimented on over 60 clips and convincing results have been achieved. © Springer-Verlag. 2004.

Publication Date

1-1-2004

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

3115

Number of Pages

279-288

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-540-27814-6_35

Socpus ID

35048831995 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/35048831995

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