Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864721 | Neurocomputing | 2018 | 12 Pages |
Abstract
In this paper, we propose an innovative classification method which combines texture features of images filtered by Gaussian derivative models with extreme learning machine (ELM). In the texture image classification, feature extraction is a very crucial step. Thusly, we use linear filters consisting of two Gaussian derivative models, difference of Gaussian (DOG) and difference of offset Gaussian (DOOG), to detect texture information of images. Besides, ensemble extreme learning machine (E2LM) is proposed to reduce the randomness of original ELM and used as the classifier in this paper. We evaluate the performance of both the texture features and the classifier E2LM by using three datasets: Brodatz album, VisTex database and Berkeley image segmentation database. Experimental results indicate that Gaussian derivative models are superior to Gabor filters, and E2LM outperforms the support vector machine (SVM) and ELM in classification accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Song Yan, Zhang Shujing, He Bo, Sha Qixin, Shen Yue, Yan Tianhong, Nian Rui, Amaury Lendasse,