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Proceedings Paper

Image indexing technique using entropy measures with a multilevel multiresolution approach
Author(s): Tae-Hee Kim; Dong-Seok Jeong
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Paper Abstract

In this paper, we propose a new content-based indexing algorithm that utilizes pixel-wise entropy and extracts features such as color and entropy from an image as indices. We propose a technique that fulfills both global and regional searching. Global searching scheme utilizes entropy features with multilevel-multiresolution. As resolution of the image is reduced, another information of the image is revealed. As gray-level of the image is reduced, we see how large the gray- level differences are between neighboring pixels. Regional searching utilizes color features that are extracted from regions separated by entropy measures. Our algorithm provides not only the automated extraction of entropy-based regions but also the representation of their color contents. Thus, we can classify images using entropy and multiresolution multi-level based features. Various experiments show the promising future of the proposed algorithm.

Paper Details

Date Published: 23 December 1997
PDF: 9 pages
Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298447
Show Author Affiliations
Tae-Hee Kim, Inha Univ. (South Korea)
Dong-Seok Jeong, Inha Univ. (South Korea)


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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