关键词: 图像分类, 深度学习, K均值聚类, 浅层网络

Abstract: Due to the high redundancy of image data, traditional image classification methods have low accuracy. Although the deep learning structures earn outperformance, they are hard to be trained. This paper proposes a shallow fuzzy K-means clustering network to do image classification. This network uses fuzzy K-means clustering to help build feature vectors, which is applied to train the shallow network classifier to do image classification tasks. The proposed algorithm can achieve better performance compared with the traditional methods. A solid foundation for further research is laid through the analysis and explanation of the experimental data.

Key words: image classification, deep learning, K-means clustering, shallow network