NDLI: A Parsimonious Self-Organizing Neuro Fuzzy System for Image Classification with Feature Ranking and Wavelet Transformations

Content Provider	IEEE Xplore Digital Library
Author	Anisha, A. Deepa, M. Tamijeselvy, P.
Copyright Year	2008
Description	Author affiliation: V.L.B. Janakiammal Coll. of Eng. & Technol., Coimbatore (Anisha, A.; Deepa, M.; Tamijeselvy, P.)
Abstract	A parsimonious self-organizing neuro fuzzy system for image classification is proposed in this paper. Self-organizing neuro fuzzy system is designed by synthesizing fuzzy clustering and support vector machine which is applied to classification problems. Fuzzy clustering decides the number of rules instead of support vectors resulting in smaller number of rules. For enhancement the most appropriate features relevant to the classification task are identified by feature ranking. Feature ranking and training are performed simultaneously in an integrated way resulting in good generalization performance. Instead of taking the whole image, the Haar discrete wavelet transform (Haar DWT) of the image is considered and only the first sub band of the image after applying Haar DWT is taken for feature extraction. We use Haralick and Texture Spectrum features as image content descriptors and efficiently employ them to retrieve images.
Starting Page	26
Ending Page	31
File Size	477233
Page Count	6
File Format	PDF
ISBN	9780769532677
DOI	10.1109/ICETET.2008.84
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Fuzzy Clustering Transforms Discrete wavelet transforms SVM Wavelet transformations Support vector machines Training Self Organized learning Feature extraction Feature ranking Fuzzy systems Image classification
Content Type	Text
Resource Type	Article

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