Abstract
The Image Classification Algorithm Based on Multi-feature Fusion and Deep Belief Networks is a method which extracts the color, texture and shape features of the image and integrates the three basic features first, and then, the fusion information is used as the input data of the deep belief networks model to train the samples and realize image classification. The results show that the classification accuracy can be improved by 21.2% compared with the image classification using a single feature. Compared with the mainstream classification algorithms, the classification accuracy can be effectively improved and it need no more time consuming.
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