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Proceedings Paper

Feature representation and compression for content-based retrieval
Author(s): Hua Xie; Antonio Ortega
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Paper Abstract

In semantic content-based image/video browsing and navigation systems, efficient mechanisms to represent and manage a large collection of digital images/videos are needed. Traditional keyword-based indexing describes the content of multimedia data through annotations such as text or keywords extracted manually by the user from a controlled vocabulary. This textual indexing technique lacks the flexibility of satisfying various kinds of queries requested by database users and also requires huge amount of work for updating the information. Current content-based retrieval systems often extract a set of features such as color, texture, shape motion, speed, and position from the raw multimedia data automatically and store them as content descriptors. This content-based metadata differs from text-based metadata in that it supports wider varieties of queries and can be extracted automatically, thus providing a promising approach for efficient database access and management. When the raw data volume grows very large, explicitly extracting the content-information and storing it as metadata along with the images will improve querying performance since metadata requires much less storage than the raw image data and thus will be easier to manipulate. In this paper we maintain that storing metadata together with images will enable effective information management and efficient remote query. We also show, using a texture classification example, that this side information can be compressed while guaranteeing that the desired query accuracy is satisfied. We argue that the compact representation of the image contents not only reduces significantly the storage and transmission rate requirement, but also facilitates certain types of queries. Algorithms are developed for optimized compression of this texture feature metadata given that the goal is to maximize the classification performance for a given rate budget.

Paper Details

Date Published: 29 December 2000
PDF: 12 pages
Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); doi: 10.1117/12.411789
Show Author Affiliations
Hua Xie, Univ. of Southern California (United States)
Antonio Ortega, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 4310:
Visual Communications and Image Processing 2001
Bernd Girod; Charles A. Bouman; Eckehard G. Steinbach, Editor(s)

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