Natural scene logo recognition by joint boosting feature selection in salient regions

Wei Fan; Jun Sun; Satoshi Naoi; Akihiro Minagawa; Yoshinobu Hotta

doi:10.1117/12.873341

24 January 2011 Natural scene logo recognition by joint boosting feature selection in salient regions

Wei Fan, Jun Sun, Satoshi Naoi, Akihiro Minagawa, Yoshinobu Hotta

Proceedings Volume 7874, Document Recognition and Retrieval XVIII; 78740W (2011) https://doi.org/10.1117/12.873341
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Logos are considered valuable intellectual properties and a key component of the goodwill of a business. In this paper, we propose a natural scene logo recognition method which is segmentation-free and capable of processing images extremely rapidly and achieving high recognition rates. The classifiers for each logo are trained jointly, rather than independently. In this way, common features can be shared across multiple classes for better generalization. To deal with large range of aspect ratio of different logos, a set of salient regions of interest (ROI) are extracted to describe each class. We ensure the selected ROIs to be both individually informative and two-by-two weakly dependant by a Class Conditional Entropy Maximization criteria. Experimental results on a large logo database demonstrate the effectiveness and efficiency of our proposed method.

Citation Download Citation

Wei Fan, Jun Sun, Satoshi Naoi, Akihiro Minagawa, and Yoshinobu Hotta "Natural scene logo recognition by joint boosting feature selection in salient regions", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740W (24 January 2011); https://doi.org/10.1117/12.873341

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