accuracy enhancement of function-oriented web image classification
We propose a function-oriented classification of web images and show new applications using this categorization. We defined nine categories of images taking into account of their functions used in web pages, and classified web images by using Support Vector Machine (SVM) in tree structured way. In order to achieve high accuracy of classification, we employed two kinds of features, image-based features and text-based features, and the two kinds can be used together or separately for the stages of the classification. We also utilized DCT coefficients to classify photo images and illustrations. As a result, accurate classification has been achieved. Finally, we show the page summarization as a new application that is made feasible for the first time by our new categories of WWW images.
web images classification support vector machine