现有基于深度学习的图像分类方法普遍缺少纹理特征的针对性，分类精度较低，难以同时适用于简单纹理和复杂纹理分类。提出一种基于自适应纹理特征融合的深度学习模型，能够结合类间差异性纹理特征做出分类决策。首先，根据纹理特征的最大类间差异性，构建图像的纹理特征图像；然后，采用原始图像与特征鲜明的纹理特征图像并行训练改进的双线性模型，获取双通道特征；最后，基于决策融合构建自适应分类模块，连接原图与纹理集的平均池化特征图进行通道权重提取，根据通道权重融合2个并行神经网络模型的分类向量，得到最优融合分类结果。在KTH-TIPS，KTH-TIPS-2b,UIUC和DTD 4个公共纹理数据集上对模型的分类性能进行评估，分别得到了99.98%、99.95%、99.99%和67.09%的准确率，表明所提模型具有普遍高效的识别性能。


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.


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