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An efficient method for texture feature extraction and recognition based on contourlet transform and canonical correlation analysis
Author
Source
Issue
Vol. 2017, Issue 29 (31 Dec. 2017), pp.498-511, 14 p.
Publisher
Wasit University Educational College
Publication Date
2017-12-31
Country of Publication
Iraq
No. of Pages
14
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Feature extraction is an important processing step in texture classification.
For feature extraction in contourlet domain, statistical features for blocks of subband are computed.
In this paper, we present an efficient feature vector extraction method for texture classification.
For more discriminative feature a canonical correlation analysis method is propose for feature vector fused to the different sample of texture in the same cluster.
The KNN (K-Nearest Neighbor) classifier is utilizing to perform texture classification.
American Psychological Association (APA)
al-Jabburi, Ali Muhsin. 2017. An efficient method for texture feature extraction and recognition based on contourlet transform and canonical correlation analysis. Journal of Education College،Vol. 2017, no. 29, pp.498-511.
https://search.emarefa.net/detail/BIM-843286
Modern Language Association (MLA)
al-Jabburi, Ali Muhsin. An efficient method for texture feature extraction and recognition based on contourlet transform and canonical correlation analysis. Journal of Education College No. 29 (2017), pp.498-511.
https://search.emarefa.net/detail/BIM-843286
American Medical Association (AMA)
al-Jabburi, Ali Muhsin. An efficient method for texture feature extraction and recognition based on contourlet transform and canonical correlation analysis. Journal of Education College. 2017. Vol. 2017, no. 29, pp.498-511.
https://search.emarefa.net/detail/BIM-843286
Data Type
Journal Articles
Language
English
Record ID
BIM-843286