Improving Logo Spotting and Matching for Document Categorization by a Post-Filter based on Homography

Digital document categorization based on logo spotting and recognition has raised a great interest in the research community because logos in documents are sources of information for categorizing documents with low costs. In this paper, we present an approach to improve the result of our method for logo spotting and recognition based on keypoint matching and presented in our previous paper [7]. First, the keypoints from both the query document images and a given set of logos (logo gallery) are extracted and described by SIFT, and are matched in the SIFT feature space. Secondly, logo segmentation is performed using spatial density-based clustering. The contribution of this paper is to add a third step where homography is used to filter the matched keypoints as a post-processing. And finally, in the decision stage, logo classification is performed by using an accumulating histogram. Our approach is tested using a well-known benchmark database of real world documents containing logos, and achieves good performances compared to state-of-the-art approaches.