Logo Spotting on Document Images using Local Features
Viet Phuong Le, Quoc Bao Dang, Cao Dê Tran · 2015
In this paper, we propose a framework for logo spotting by using local features. We present key-point matching methods to match local features of logo images to those of document images. For segmentation, a density-based clustering method is used to group matches and remove the outliers. Then, a two-stage algorithm based on homography with RANSAC is used to verify and localize the spotting results. In our experiments, many kinds of local features are employed and compared for their effectiveness. The results show that SIFT and SURF outperform in terms of accuracy.