Motion Frame Analysis and Scene Abstraction: Discrimination Ability of Fuzziness Measures

Article type: Research Article

Authors: Pal, Sankar K.^; | Leigh, Albert B.^;

Affiliations: NASA Johnson Space Center, Software Technology Branch/PT4, Houston, TX 77058, e-mail: sankar@gothamcity.jsc. nasa.gov and leigh@gothamcity.jsc.nasa.gov

Note: [] Prof. Pal is on leave from the Machine Intelligence Unit, Indian Statistical Institute, Calcutta 700035, India. E-mail: sankar@isical.ernet.in.

Note: [] Albert Leigh is currently employed as a senior engineer at LinCom Corporation, 1020 Bay Area Blvd. #200, Houston, TX 77058.

Abstract: This article addresses a solution to the problem of scene estimation/abstraction of motion video data in the fuzzy set theoretic framework. Using various fuzzy geometrical and information measures as image features, an algorithm is developed to compute the change of information in each of two successive frames to classify scenes/frames. Frame similarity is measured in terms of weighted distance in fuzzy feature space. This categorization process of raw input visual data can be used to establish structure for correlation. The investigation not only attempts to determine the discrimination ability of the fuzziness measures for classifying scenes, but also enhances the capability of nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

DOI: 10.3233/IFS-1995-3306

Journal: Journal of Intelligent and Fuzzy Systems, vol. 3, no. 3, pp. 247-256, 1995