An Image Classification Method Based on Multi-feature Fusion and Multi-kernel SVM
| Autor: | Xueqiang Lv, Zixi Xiang, Kai Zhang |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Contextual image classification
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Linear classifier Support vector machine ComputingMethodologies_PATTERNRECOGNITION Automatic image annotation Image texture Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence business Feature detection (computer vision) |
| Zdroj: | 2014 Seventh International Symposium on Computational Intelligence and Design. |
| Popis: | Content-based image classification is such a technique which adapt to mass image data access and classification operation and it is based on the color, texture and shape feature. Image automatic classification using computer is one of the current hot topic. The traditional image classification method based on a single feature is ineffective. In this paper, we use multi-kernel SVM classifiers and the multi-feature fusion of feature weighting for image classification. Feature weighting is to set a certain weight for various features according to a certain standards and it is an effective way to find the most effective features. We use Corel Image Library as the database. The experimental result shows that the accuracy of image classification based on multi-feature fusion with multi-kernel SVM is much higher than a single feature. The method in this paper is an effective approach to improve the accuracy of image classification and expand possibilities for other application. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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