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Titolo:
Structural feature indexing for retrieval of partially visible shapes
Autore:
Nishida, H;
Indirizzi:
Ricoh Co Ltd, Software Res Ctr, Bunkyo Ku, Tokyo 1120002, Japan Ricoh Co Ltd Tokyo Japan 1120002 es Ctr, Bunkyo Ku, Tokyo 1120002, Japan
Titolo Testata:
PATTERN RECOGNITION
fascicolo: 1, volume: 35, anno: 2002,
pagine: 55 - 67
SICI:
0031-3203(200201)35:1<55:SFIFRO>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
OBJECT RECOGNITION; DATA MANAGEMENT; MODELS;
Keywords:
image database; image retrieval; multimedia document; shape query; feature indexing;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
30
Recensione:
Indirizzi per estratti:
Indirizzo: Nishida, H Ricoh Co Ltd, Software Res Ctr, Bunkyo Ku, 1-1-17 Koishikawa, Tokyo 1120002, Japan Ricoh Co Ltd 1-1-17 Koishikawa Tokyo Japan 1120002 0002, Japan
Citazione:
H. Nishida, "Structural feature indexing for retrieval of partially visible shapes", PATT RECOG, 35(1), 2002, pp. 55-67

Abstract

Efficient and robust information retrieval from large image databases is an essential functionality for the reuse, manipulation, and editing of multimedia documents. Structural feature indexing is a potential approach to efficient shape retrieval from large image databases, but the indexing is sensitive to noise, scales of observation, and local shape deformations. It hasnow been confirmed that efficiency of classification and robustness against noise and local shape transformations can be improved by the feature indexing approach incorporating shape feature generation techniques (Nishida, Comput. Vision Image Understanding 73 (1) (1999) 121-136). In this paper, based on this approach, an efficient, robust method is presented for retrieval of model shapes that have parts similar to the query shape presented to the image database. The effectiveness is confirmed by experimental trials with a large database of boundary contours obtained from real images, and is validated by systematically designed experiments with a large number of synthetic data. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 15/07/20 alle ore 20:33:33