Result: Shape retrieval using statistical chord-length features

Title:
Shape retrieval using statistical chord-length features
Source:
Advances in image and video technology (First pacific rim symposium, PSIVT 2006, Hsinchu, Taiwan, December 10-13, 2006)0PSIVT 2006. :403-410
Publisher Information:
Berlin; Heidelberg: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 13 ref 1
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Merchant Marine College, Shanghai Maritime University, Shanghai, 200135, China
Department of Computer Science and Engineering, Fudan University, Shanghai, 200433, China
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19008248
Database:
PASCAL Archive

Further Information

A novel shape description method, statistical chord-length features (SCLF), is proposed for shape retrieval. SCLF first describes the contour of a 2D shape using k/2 one-dimensional chord-length functions derived from partitioning the contour into k arcs of the same length, where k is the parameter of SCLF. The means and variances of all the chord-length functions are then calculated and a k dimensional feature vector is generated as a shape descriptor. Two experiments are conducted and the results show that SCLF achieves higher retrieval performance than traditional description methods such as geometric moment invariants and Fourier descriptors.