title: Extreme learning machine and adaptive sparse representation for image classification.
abstract: Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.
authors.affiliation: Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Zhejiang, 310018, China. Electronic address: jwcao@hdu.edu.cn.
authors.forename: Jiuwen
authors.initials: J
authors.lastname: Cao
authors.affiliation: Department of Mathematics, China Jiliang University, Hangzhou, Zhejiang Province, 310018, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Zhang
authors.affiliation: Department of Mathematics, China Jiliang University, Hangzhou, Zhejiang Province, 310018, China.
authors.forename: Minxia
authors.initials: M
authors.lastname: Luo
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
authors.forename: Chun
authors.initials: C
authors.lastname: Yin
authors.affiliation: Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Zhejiang, 310018, China.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D006801:Humans; D000069550:Machine Learning / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000145:classification* / Q000379:methods*; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation / Q000379:methods; D015203:Reproducibility of Results
keywords: Extreme learning machine; Image classification; Leave-one-out cross validation; Sparse representation