Image classification using the web graph

Image classification is a well-studied and hard problem in computer vision. We extend a proven solution for classifying web spam to handle images. We exploit the link structure of the web graph: a web page related to a given category is normally linked to other pages describing related objects. Our approach combines information from the webgraph structure with semi-supervised learning from all the unlabeled images to create a superior image-classification model for multimedia data. We show that fusing image, text and web-graph features gives a 12% improvement (in the area under the ROC curve) over content features alone in an adult image-classification experiment. Categories and Subject Descriptors I.5.2 [Pattern Recognition]: Design Methodology-Classifier design and evaluation General Terms Algorithms Keywords Algorithm, image classification, web graph

• Type: Conference
• Year: 2010
• Where: MM
• Authors: Dhruv Kumar Mahajan, Malcolm Slaney
• Keywords: Image, Image Classification, MM 2010, Multimedia, Superior Image-classification Model