Image classification based on effective extreme learning machine

Authors:
Feilong Cao;Bo Liu;Dong Sun Park
Affiliations:
Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, PR China;Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, PR China;Department Electronics and Information Engineering, Chonbuk National University, 664-141 Ga Deokjin-Dong, Jeonju, Jeonbuk, 561-756, South Korea
Venue:
Neurocomputing
Year:
2013

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Abstract

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.