关键词: 纹理分类, 卷积神经网络（CNN）, 计算机视觉

Abstract: Deep convolutional neural network (CNN) has recently achieved great breakthroughs in many computer vision tasks. However, its application in texture classification has not been thoroughly researched. To this end, this paper carries out a systemic research on its application in image texture classification. Specifically, CNN is used to extract preliminary image feature, and subsequent PCA (principal component analysis) operation can reduce its dimensionality to obtain final texture feature which is fed into an SVM (support vector machine) classifier for prediction. This paper does comprehensive experiments and analysis on four benchmark datasets. The results show that CNN is a better texture feature representation and achieves quite good performance in most image texture datasets. However, CNN performs worse in datasets with image noise and rotation. Thus, this paper indicates the necessity to enhance the abilities of noise tolerance and rotation invariance of CNN, and it is necessary to construct a large diverse texture dataset to guarantee its best performance in image texture classification.

Key words: texture classification, convolutional neural network (CNN), computer vision