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Proceedings Paper

Models for automatic classification of video sequences
Author(s): Giridharan Iyengar; Andrew B. Lippman
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Paper Abstract

In this paper, we explore a technique for automatic classification of video sequences, (such as a TV broadcast, movies). This technique analyzes the incoming video sequences and classifies them into categories. It can be viewed as an on-line parser for video signals. We present two techniques for automatic classification. In the first technique, the incoming video sequence is analyzed to extract the motion information. This information is optimally projected onto a single dimension. This projection information is then used to train Hidden Markov Models (HMMs) that efficiently and accurately classify the incoming video sequence. Preliminary results with 50 different test sequences (25 Sports and 25 News sequences) indicae a classification accuracy of 90% by the HMM models. In the second technique, 24 full-length motion picture trailers are classified using HMMs. This classification is compared with the internet movie database and we find that they correlate well. Only two out of 24 trailers were classified incorrectly.

Paper Details

Date Published: 23 December 1997
PDF: 12 pages
Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298454
Show Author Affiliations
Giridharan Iyengar, Massachusetts Institute of Technology (United States)
Andrew B. Lippman, Massachusetts Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3312:
Storage and Retrieval for Image and Video Databases VI
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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