Abstract: The existing image classification methods based on deep learning generally lack the pertinence of texture features, and have low classification accuracy, which is difficult to be applied to the classification of simple texture and complex texture. A deep learning model based on adaptive texture feature fusion is proposed, which can make classification decisions based on differential texture features between classes. Firstly, the texture feature image is constructed according to the difference between the largest categories of texture features. Secondly, the improved bilinear model is trained in parallel with the original image and the distinctive texture feature image to obtain the dual-channel features. Finally, an adaptive classification module is constructed based on decision fusion, the channel weight is extracted by the average pooling feature map connecting the original image and texture map. The optimal fusion classification result is obtained by fusing the classification vector of two parallel neural network models according to the channel weight. The classification performance of the algorithm was evaluated on four common texture data sets, namely KTH-TIPS, KTH-TIPS-2b,UIUC and DTD, and the accuracy rates are 99.98%, 99.95%, 99.99% and 67.09%, respectively, indicating that the proposed recognition method has generally efficient recognition performance.

Key words: texture classification, decision fusion, deep learning, bilinear CNN, ResNet