Abstract
Recent years have shown us the quick development of social network. For companies, microblog platform is more and more important as one source to disseminate brand information and monitor their development. Compared with the frequently used text information existing in traditional media, microblog platform provides information about brands in more types such as images and other related information forms. According to the statistics, microblogs posted on social network contain more and more percentage of images. Hence how to recognize logos in images from social network is of high value. To address this problem, we propose a novel learning-based logo detection method with social network information assistance. A new dense histogram type feature is proposed to classify logo and non-logo image patches. To increase the detection precision, social network content is analyzed and employed to do filtering to reduce detection window candidates. Through the evaluation on large-scale data collected from Sina Weibo platform, the proposed method is demonstrated effective.
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