A Novel Support Vector Machine Fuzzy Network for Image Classification Using MPEG-7 Visual Descriptors

MPEG-7 standard describes visual descriptors and performance metrics for image classification. Amongst all image features, color and texture are more visually expressive and hence are attractive for visual descriptors. Further, combination of features makes image classification more relevant and robust. This paper proposes efficient methods for image classification using 3 MPEG-7 descriptors to represent color and texture features. Additionally, we apply Support Vector Machine and Fuzzy algorithm to extract linguistic fuzzy rules, in order to bridge the "semantic gap" between the lower-lever descriptors and the high-level semantic of an image. The Support Vector Machine Fuzzy Network was evaluated using images from the aceMedia Repository and more specifically in a beach/urban scenes classification problem.