Gaussian derivative models and ensemble extreme learning machine for texture image classification
| Autor: | Rui Nian, Tianhong Yan, Qixin Sha, Yue Shen, Bo He, Amaury Lendasse, Yan Song, Shujing Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Difference of Gaussians
Computer science Cognitive Neuroscience Gaussian Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology symbols.namesake Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Computer vision Extreme learning machine Contextual image classification business.industry 020206 networking & telecommunications Pattern recognition Image segmentation Computer Science Applications Support vector machine ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition symbols 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) |
| Zdroj: | Neurocomputing. 277:53-64 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2017.01.113 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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