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Proceedings Paper

Visual shape retrieval using multiscale term distributions
Author(s): Boaz J. Super
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Paper Abstract

This paper presents a method for fast and effective similarity-based shape retrieval. Shape similarity is determined by comparing the frequencies with which different types of local structure occur in each shape. The system consists of three processes. (1) The segmentation process uses a scale-space approach to find convex segments that lie between curvature zero-crossings at all scales. Local shape structure is represented by short sequences of segments, called terms. (2) The representation process classifiers the terms into types based on a set of local shape features. Then the distribution of term types within the shape is computed. (3) The retrieval process compares the term type distribution of the query shape to the term type distributions of the database shapes and retrieves the most similar database shapes. Efficient data structures are used to store the distributions compactly and to support fast retrieval. The performance of the method on a test database ranged from 69 percent to 100 percent of ideal performance, depending on the number of items retrieved.

Paper Details

Date Published: 23 December 1999
PDF: 12 pages
Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373553
Show Author Affiliations
Boaz J. Super, Univ. of Illinois/Chicago (United States)


Published in SPIE Proceedings Vol. 3972:
Storage and Retrieval for Media Databases 2000
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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