A new K-View algorithm for texture image classification using rotation-invariant feature

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A new K-View algorithm for texture image classification using rotation-invariant feature

Full text

Pdf (419 KB)

Source

Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents

Honolulu, Hawaii

SESSION: Computational intelligence and image analysis track table of contents

Pages 914-921

Year of Publication: 2009

ISBN:978-1-60558-166-8

Authors

Hong Liu	Huazhong University of Science and Technology, Wuhan, China
Siguang Dai	Huazhong University of Science and Technology, Wuhan, China
Enmin Song	Huazhong University of Science and Technology, Wuhan, China
Cihui Yang	Huazhong University of Science and Technology, Wuhan, China
Chih-Cheng Hung	Southern Polytechnic State University, Marietta, GA

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 14, Downloads (12 Months): 60, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1529282.1529481 What is a DOI?

ABSTRACT

This paper proposes a new K-View algorithm for texture image classification using rotation-invariant features. These features are statistically derived from characteristic view sets for each texture. Unlike the existing K-View algorithm, all the views used are transformed into rotation-invariant features and the K views are selected randomly. In contrast, the existing K-View algorithm uses the K-means algorithm for choosing the K views. In this new algorithm the decision of determining a pixel to which texture class it belong to, is made by considering all the views which consist of the pixel being classified. In order to preserve the primitive information of a texture class as much as possible, the proposed algorithm randomly selects k views of the view set from each sample sub-image as the characteristic view set. Experimental results show that the proposed algorithm is more robust and accurate compared with the results of the existing K-View algorithm.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Carlos Avilés-Cruz , Risto Rangel-Kuoppa , Mario Reyes-Ayala , A. Andrade-Gonzalez , Rafael Escarela-Perez, High-order statistical texture analysis: font recognition applied, Pattern Recognition Letters, v.26 n.2, p.135-145, 15 January 2005 [doi>10.1016/j.patrec.2004.09.038]
	2	D. A. Clausi, "An analysis of co-occurrence texture statistics as a function of grey level quantization," CANADIAN JOURNAL OF REMOTE SENSING, vol. 28, pp. 45--62, 2002.
	3	C. Palm, "Color texture classification by integrative Co-occurrence matrices," Pattern Recognition, vol. 37, pp. 965--976, 2004.
	4	M. A. Savelonas , D. K. Iakovidis , D. E. Maroulis, An LBP-Based Active Contour Algorithm for Unsupervised Texture Segmentation, Proceedings of the 18th International Conference on Pattern Recognition, p.279-282, August 20-24, 2006 [doi>10.1109/ICPR.2006.260]
	5	D. A. Clausi, "Comparison and Fusion of Co-occurrence, Gabor and MRF Texture Features for Classification of SAR Sea-Ice Imagery," Atmosphere-Ocean, vol. 39, pp. 183--194, 2001.
	6	S. E. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of texture features based on Gabor filters," IEEE Transactions on Image Processing, vol. 11, pp. 1160--1167, 2002.
	7	Jacques Brochard , Majdi Khoudeir , Bertrand Augereau, Invariant feature extraction for 3D texture analysis using the autocorrelation function, Pattern Recognition Letters, v.22 n.6-7, p.759-768, May 2001 [doi>10.1016/S0167-8655(01)00015-0]
	8	C. Hung, S. Yang, and C. Laymon, "Use of Characteristic Views in Image Classification," INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, vol. 16, pp. 949--952, 2002.
	9	S. Yang and C. C. Hung, "Texture classification in remotely sensed images," SoutheastCon, 2002. Proceedings IEEE, pp. 62--66, 2002.
	10	Shisong Yang , Chih-Cheng Hung, Image texture classification using datagrams and characteristic views, Proceedings of the 2003 ACM symposium on Applied computing, March 09-12, 2003, Melbourne, Florida [doi>10.1145/952532.952538]
	11	Madasu Hanmandlu , Vamsi Krishna Madasu , Shantaram Vasikarla, A Fuzzy Approach to Texture Segmentation, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2, p.636, April 05-07, 2004
	12	C. C. Hung, M. Pham, S. Arasteh, B. C. Kuo, and T. Coleman, "Image Texture Classification Using Texture Spectrum and Local Binary Pattern," IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006, pp. 2750--2753, 2006.
	13	E. Song, R. Jin, C. C. Hung, Y. Luo, and X. Xu, "Boundary Refined Texture Segmentation Based on K-Views and Datagram Methods," IEEE Symposium on Computational Intelligence in Image and Signal Processing, 2007. CIISP 2007, pp. 19--23, 2007.