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Proceedings Paper

Evolving discriminators for querying video sequences
Author(s): Giridharan Iyengar; Andrew B. Lippman
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Paper Abstract

In this paper we present a framework for content based query and retrieval of information from large video databases. This framework enables content based retrieval of video sequences by characterizing the sequences using motion, texture and colorimetry cues. This characterization is biologically inspired and results in a compact parameter space where every segment of video is represented by an 8 dimensional vector. Searching and retrieval is done in real- time with accuracy in this parameter space. Using this characterization, we then evolve a set of discriminators using Genetic Programming Experiments indicate that these discriminators are capable of analyzing and characterizing video. The VideoBook is able to search and retrieve video sequences with 92% accuracy in real-time. Experiments thus demonstrate that the characterization is capable of extracting higher level structure from raw pixel values.

Paper Details

Date Published: 15 January 1997
PDF: 12 pages
Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); doi: 10.1117/12.263404
Show Author Affiliations
Giridharan Iyengar, MIT Media Lab. (United States)
Andrew B. Lippman, MIT Media Lab. (United States)

Published in SPIE Proceedings Vol. 3022:
Storage and Retrieval for Image and Video Databases V
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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