A Contour-Based Method for Logo Detection

This paper presents a new approach for logo detection exploiting contour based features. At first stage, pre-processing, contour detection and line segmentation are done. These processes result in set of Outer Contour Strings (OCSs) describing each graphics and text parts of the documents. Then, the logo detection problem is defined as a region scoring problem. Two types of features, coarse and finer ones, are computed from each OCS. Coarse features catch graphical and domain information about OCSs, such as logo positions and aspect ratios. Finer features characterize the contour regions using a gradient based representation. Using these features, we employ regression fitting to score how likely an OCS takes part of a logo region. A final step of correction helps with the wrong segmentation cases. We present experiments done on the Tobacco-800 dataset, and compare our results with the literature. We obtain interesting results compared to the best systems.

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