A Novel Fuzzy Visual Object Classification Approach

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2012

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Ieee

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Information Systems Engineering
Information Systems is an academic and professional discipline which follows data collection, utilization, storage, distribution, processing and management processes and modern technologies used in this field. Our department implements a pioneering and innovative education program that aims to raise the manpower, able to meet the changing and developing needs and expectations of our country and the world. Our courses on current information technologies especially stand out.

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Abstract

Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of the proposed Fuzzy SVM compared to the traditional SVM.

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fuzzy suppor vector machines, membership function, image classification, self-organizing maps

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) -- JUN 10-15, 2012 -- Brisbane, AUSTRALIA

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https://hdl.handle.net/20.500.14411/8672

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