A framework for semantic classification of scenes using Finite State Machines

    Authors

    Y. Zhai; Z. Rasheed;M. Shah

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Keywords

    Computer Science, Artificial Intelligence; Computer Science, Information; Systems; Computer Science, Theory & Methods; Imaging Science &; Photographic Technology

    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.

    Journal Title

    Image and Video Retrieval, Proceedings

    Volume

    3115

    Publication Date

    1-1-2004

    Document Type

    Article

    Language

    English

    First Page

    279

    Last Page

    288

    WOS Identifier

    WOS:000223080700031

    ISSN

    0302-9743; 3-540-22539-0

    Share

    COinS