改进卷积玻尔兹曼机的图像特征深度提取

针对卷积深度和信念网络存在计算复杂度高和训练缓慢的问题，提出卷积深度玻尔兹曼机用于图像特征提取.针对卷积受限玻尔兹曼机进行改进，提出最大化图像中间区域概率的训练目标函数，并引入性能较好的交叉熵稀疏惩罚因子和dropout训练方法.设计卷积深度玻尔兹曼机结构，提出均值聚合机制，将聚合层内点的值定义为block中各点激活概率均值，对层间关联进行简化，将聚合层内各面直接叠加以供高层CRBM提取特征.通过在MNIST手写数字识别集上的实验结果证明，采用新模型提取的图像特征分类准确率提高0.5%、训练时间减少50%，且达到了目前MNIST数据集的最佳水平.
To resolve the problems of high computational complexity and slow training in Convolutional Deep Belief Net, Convolutional Deep Boltzmann Machine (CDBM) is proposed to extract image features. To improve the Convelution Restricted Boltzmann Machine(CRBM), a new training objective function to maximize the probability of intermediate image area is proposed, along with introducing the cross-entropy penalty factor and dropout training. After that, CDBM is designed based on modified CRBM. The mean-pool mechanism is presented to lessen computational complexity and improve the robustness of features for image scaling. The relationship between layers is simplified to extract high-level abstract features. The MNIST handwritten digits database is used to test this new model and the results prove that features extracted by CDBM are more accurate than CDBN. The classification accuracy rate increase at least 0.5%, and training time decrease more than 50%.