Fast Genre Classification of Web Images Using Global and Local Features

Abstract

A number of images are present on the Web and the number is increasing every day. To effectively mine the contents embedded in Web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis, such as text and non-text image discrimination. We herein propose a hierarchical algorithm for efficiently classifying Web images into four classes, namely, natural scene images, born-digital images, scanned and cameracaptured paper documents, which are the most prevalent image types on the Web. Our algorithm consists of two stages; the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first-stage classifier is processed by the second stage, which further extracts local texture features represented in the Bag-of-Words framework and uses another SVM classifier for final classification. In addition, we design two fusion strategies to train the second classifier and generate the final prediction label depending on the usage of local features in the second stage. To validate the effectiveness of our proposed method, we also build a database containing more than 55,000 images from various sources. On our test image set, we obtained an overall classification accuracy of 98.4% and the processing speed is over 27FPS on an Intel(R) Xeon(R) CPU (2.90GHz).