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Proceedings Paper

Automatic picture orientation detection based on classifier combination
Author(s): Yuejia Sun; Changsong Liu; Xiaoqing Ding; Zhigang Fan; Francis Tse
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Paper Abstract

Automatic picture orientation recognition is of great significance in many applications such as consumer gallery management, webpage browsing, content-based searching or web printing. We try to solve this high-level classification problem by relatively low-level features including Spacial Color Moment (CM) and Edge Direction Histogram (EDH). An improved distance-based classification scheme is adopted as our classifier. We propose an input-vector-rotating strategy, which is computationally more efficient than several conventional schemes, instead of collecting and training samples for all four classes. Then we research on the classifier combination algorithm to make full use of the complementarity between different features and classifiers. Our classifier combination methods include two levels: feature-level and measurement-level. And we present two classifier combination structures (parallel and cascaded) at measurement-level with a rejection option. As the precondition of measurement-level methods, the theory of Classifier's Confidence Analysis (CCA) is introduced with the definition of concepts such as classifier's confidence and generalized confidence. The classification system finally approached 90% recognition accuracy on a wide unconstrained consumer picture set.

Paper Details

Date Published: 7 February 2011
PDF: 15 pages
Proc. SPIE 7879, Imaging and Printing in a Web 2.0 World II, 78790C (7 February 2011); doi: 10.1117/12.880046
Show Author Affiliations
Yuejia Sun, Tsinghua Univ. (China)
Changsong Liu, Tsinghua Univ. (China)
Xiaoqing Ding, Tsinghua Univ. (China)
Zhigang Fan, Xerox Corp.
Francis Tse, Xerox Corp.


Published in SPIE Proceedings Vol. 7879:
Imaging and Printing in a Web 2.0 World II
Qian Lin; Jan P. Allebach; Zhigang Fan, Editor(s)

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