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Proceedings Paper

Informative and compressed features for aircraft detection in object recognition system
Author(s): Jiandan Zhong; Qinzhang Wu; Tao Lei; Guangle Yao; Kelin Sun
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Paper Abstract

It is a challenging task to build efficient and robust model for aircraft detection. In our object recognition system, aircraft detection is a main task, which faces various problems, such as blur, occlusion, and shape variation and so on. Existing approaches always require a set of complex classification model and a large number of training samples, which is inefficient and costly. In order to deal with these problems, we employ location based informative features to reduce the complexity of training data. With the employment of location based informative features, simple classifiers will manifest high performance instead of complex classifier which requires more complicated strategy for training. Further, our system needs to update the model frequently which is similar to online learning method, in order to reducing computational complexity, a very sparse measurement matrix is applied to extract features from feature space. The construction of this sparse matrix is based on the theory of sparse representation and compressed sensing. From the experimental results, the detection rate and cost of our proposed method is better than other traditional method.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003331 (29 August 2016); doi: 10.1117/12.2243777
Show Author Affiliations
Jiandan Zhong, Institute of Optics and Electronics (China)
Univ. of Electronic Science and Technology of China (China)
Qinzhang Wu, Institute of Optics and Electronics (China)
Tao Lei, Institute of Optics and Electronics (China)
Guangle Yao, Institute of Optics and Electronics (China)
Kelin Sun, Institute of Optics and Electronics (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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