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Proceedings Paper

Scene recognition based on integrating active learning with dictionary learning
Author(s): Chengxi Wang; Xueyan Yin; Lin Yang; Chengrong Gong; Caixia Zheng; Yugen Yi
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Paper Abstract

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061514 (10 April 2018); doi: 10.1117/12.2302487
Show Author Affiliations
Chengxi Wang, Jilin Univ. (China)
Xueyan Yin, Northeast Normal Univ. (China)
Lin Yang, Northeast Normal Univ. (China)
Chengrong Gong, Northeast Normal Univ. (China)
Caixia Zheng, Northeast Normal Univ. (China)
Yugen Yi, Jiangxi Normal Univ. (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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