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Proceedings Paper

Natural scene logo recognition by joint boosting feature selection in salient regions
Author(s): Wei Fan; Jun Sun; Satoshi Naoi; Akihiro Minagawa; Yoshinobu Hotta
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Paper 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.

Paper Details

Date Published: 24 January 2011
PDF: 7 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740W (24 January 2011); doi: 10.1117/12.873341
Show Author Affiliations
Wei Fan, Fujitsu Research and Development Ctr. Co., Ltd. (China)
Jun Sun, Fujitsu Research and Development Ctr. Co., Ltd. (China)
Satoshi Naoi, Fujitsu Research and Development Ctr. Co., Ltd. (China)
Akihiro Minagawa, Fujitsu Labs., Ltd. (Japan)
Yoshinobu Hotta, Fujitsu Labs., Ltd. (Japan)


Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)

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