Logo Retrieval with Representation Error of Self-taught Encoding

Logo retrieval in real-world scenarios has numerous potential applications in computer vision. Due to occlusion, illumination, non-rigid distortion and other reasons, the accuracy of feature matching in natural images is far lower than that in the print objects. For such a challenging task, a lot of papers have conducted very fruitful work. The algorithm finds approximate matching points in the images by locality sensitive hashing algorithm. Given matched points' position information, matched points are divided into several groups. With RANSAC algorithm, each group of matched points are divided into inlier points set and outlier points set, the candidate windows of logos can be mapped. Finally by calculating the representation error score of overlapping candidate windows, the lower score regions are eliminated, and the higher score regions are remained. The result of experiment shows that our approach can effectively locate more than one logo areas in an image, improving the recall of retrieval. And it also improves the mean Average Precision scores greatly by sorted files with representation error score.