NDLI: Centroid neural network with Chi square distance measure for texture classification

Content Provider	IEEE Xplore Digital Library
Author	Vu Thi Lan Huong Dong-Chul Park Dong-Min Woo Yunsik Lee
Copyright Year	2009
Description	Author affiliation: Korea Electronics Tech. Inst., Songnam, Korea (Yunsik Lee) \|\| Department of Information Engineering, Myong Ji University, YongIn, Korea (Vu Thi Lan Huong; Dong-Chul Park; Dong-Min Woo)
Abstract	An unsupervised competitive neural network for efficient classification of image textures is proposed. The proposed neural network architecture, called centroid neural network with Chi square distance measure $(CNN-χ^{2}),$ employs the Chi square measure as its distance measure and utilizes the local binary pattern (LBP) as an effective feature extraction tool for image data. The proposed $CNN-χ^{2}$ is applied to image texture classification problems on the Brodatz texture album database. The results are compared with those of conventional approaches including the HMT (hidden Markov tree), IMM (independence mixture model), and WES (wavelet energy signatures). The evaluated results demonstrate that the proposed $CNN-χ^{2}$ classification algorithm outperforms the conventional algorithms in terms of classification accuracy.
Starting Page	1310
Ending Page	1315
File Size	3622598
Page Count	6
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178865
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Cellular neural networks Hidden Markov models Image texture analysis Clustering algorithms Image texture Feature extraction Wavelet analysis Discrete wavelet transforms Euclidean distance
Content Type	Text
Resource Type	Article

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