Web image mining using concept sensitive Markov stationary features

With the explosive growth of Web resources, how to mine semantically relevant images efficiently becomes a challenging and necessary task. In this paper, we propose a concept sensitive Markov stationary feature (C-MSF) to represent images and also present a classifier based scheme for web image mining. First, through analyzing the results of Google Image Searcher, we collect an image set, which are highly relevant to a concept. Then the image set is explored to learn a C-MSF about the concept by the algorithm of random walk with restart (RWR), in which the spatial co-occurrence of the bag-of-words representation and the concept information are integrated. Obtaining the concept sensitive representation, SVM is applied to mine the web images, while the highly relevant set are considered as positive examples and other random images as negative ones. Finally, experiments on a crawled web dataset demonstrate the improved performance of the proposed scheme.