15 January 1997 Evolving discriminators for querying video sequences
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Giridharan Iyengar, Andrew B. Lippman, "Evolving discriminators for querying video sequences", Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); doi: 10.1117/12.263404; https://doi.org/10.1117/12.263404
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