Image recognition based on invariant moment in the projection space

Author

Li, Jun-Hong ; Pan, Quan ; Cui, Pei-Ling ; Zhang, Hong-cai ; Cheng, Yong-mei

Author_Institution

Sch. of Autom., Northwestern Polytech Univ., Xi´´an, China

Volume

6

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

3606

Abstract

This paper proposes a projection-based invariant moment for image recognition. A set of features invariant to image translation and scaling are obtained in the 1-D projection space. For getting rotational invariance, rapid transform is employed. After obtaining the invariant feature vector, threshold analysis is used for feature data optimization, and principle component analysis (PCA) is applied for feature data length compression. Experimental results show the superiority of our method over Hu and other invariant moments.

Keywords

data compression; image recognition; optimisation; principal component analysis; transforms; feature data length compression; feature data optimization; image recognition; image translation; invariant feature vector; principle component analysis; projection space; rapid transform; threshold analysis; Automation; Data compression; Discrete transforms; Image coding; Image recognition; Machine learning; Optimization methods; Pattern recognition; Principal component analysis; Propulsion;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1380419

Filename

1380419