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Proceedings Paper

Image representation and image similarity computation for images with multiple and partially occluded objects
Author(s): Linhui Jia; Leslie Kitchen
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Paper Abstract

This paper proposes an approach to object-based image retrieval for images contain multiple and partially occluded objects. In this approach, contours of objects are used to distinguish different classes of objects in images. We decompose all the contours in an image into segments and compute features from the segments. The C4.5 decision-tree learning algorithm is used to classify each segment in the images. Each image is represented in a k-dimensional space, where k is the number of classes of objects in all the images. Each dimension represents information about one of the classes. Euclidean distance between images in the k- dimensional space is adopted to compute similarities between images based on probabilities of segment classes. Experimental results show that this approach is effective.

Paper Details

Date Published: 1 January 2001
PDF: 11 pages
Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); doi: 10.1117/12.410951
Show Author Affiliations
Linhui Jia, Univ. of Melbourne (Australia)
Leslie Kitchen, Univ. of Melbourne (Australia)


Published in SPIE Proceedings Vol. 4315:
Storage and Retrieval for Media Databases 2001
Minerva M. Yeung; Chung-Sheng Li; Rainer W. Lienhart, Editor(s)

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