Texture feature extraction and selection for classification of images in a sequence

Authors:
Khin Win;Sung Baik;Ran Baik;Sung Ahn;Sang Kim;Yung Jo
Affiliations:
College of Electronics and Information Engineering, Sejong University, Seoul, Korea;College of Electronics and Information Engineering, Sejong University, Seoul, Korea;Dept. of Computer Engineering, Honam University, Gwangju, Korea;School of Management Information System, Kookmin University, Seoul, Korea;College of Electronics and Information Engineering, Sejong University, Seoul, Korea;College of Electronics and Information Engineering, Sejong University, Seoul, Korea
Venue:
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Year:
2004

Citing 3
Cited 0

Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering methods for texture discrimination

Pattern Recognition Letters
A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms

Pattern Recognition

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Abstract

This paper presents texture feature extraction and selection methods for on-line pattern classification evaluation. Feature selection for texture analysis plays a vital role in the field of image recognition. Despite many approaches done previously, this research is entirely different from them since it comes from the fundamental ideas of feature selection for image retrieval. The proposed approach is capable of selecting the best features without recourse to classification and segmentation procedures. In this approach, probability density function estimation and a modified Bhattacharyya distance method are applied for clustering texture features of images in sequences and for comparing multi-distributed clusters with one another, respectively.