期刊
NEUROCOMPUTING
卷 102, 期 -, 页码 90-97出版社
ELSEVIER
DOI: 10.1016/j.neucom.2012.02.042
关键词
Image classification; Curvelet transform; Discriminative locality alignment; Extreme k-means; Effective extreme learning machine
资金
- Second stage of Brain Korea [21]
- National Natural Science Foundation of China [61101240]
- Zhejiang Provincial Natural Science Foundation of China [Y6110117]
- Foundation of Innovation Team of Science and Technology of Zhejiang Province of China [2009R50024]
In this work, a new image classification method is proposed based on extreme k-means (EKM) and effective extreme learning machine (EELM). The proposed method has image decomposition with curvelet transform, reduces dimensionality with discriminative locality alignment (DLA), generates a set of distinctive features with EKM, and has a classification with EELM. Since EKM has a better clustering performance than k-means and EELM has a better accuracy than ELM, the proposed EKM-EELM algorithm has a significant improvement in classification rate. Extensive experiments are performed using challenging databases and results are compared against state of the art techniques. Experimental results show that the proposed method has superior performances on classification rate than some other traditional methods for image classification. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
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