Hybrid Featured based Pyramid Structured CNN for Texture Classification Texture is always considered as the preconscious for human vision. Texture also remains the same significance in computer vision field that can be used to help in detection, segmentation and classification tasks. Since texture is a global feature inherent in an image, containing essential surface information, which can be described in detail and hardly affected by image noises. We propose a novel endtoend structure to make use of hybrid features by a mixture network and improve the classification accuracy, mainly combining Gray Level Cooccurrence Matrix (GLCM) statistical features together with pyramid structured deep convolutional neural networks (Pyramid CNNs) features in a paralleling network structure. Considering GLCM is a remarkable descriptor for texture statistical features, it can compensate the missing information in the convolution and pooling process of CNN and decline overfitting problems. Meanwhile, multiresolution image pyramid structured CNN helps to capture both global features and local features. Quantitively, we carry out experiments on widely used datasets and results show that the GLCM and Pyramid CNN features merged structure obtains maximum 6.8% improvement comparing to the basic CNN methods.

Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval Abstract   Increasing the depth of Convolutional Neural Networks (CNNs) has been recognized to provide better generalization performance. However, in the case of 3D CNNs, stacking layers increases the number of learnable parameters linearly, making it more prone to learn redundant features. In this paper, we propose a novel 3D CNN structure that learns shared 2D triplanar features viewed from the three orthogonal planes, which we term S3PNet. Due to the reduced dimension of the convolutions, the proposed S3PNet is able to learn 3D representations with substantially fewer learnable parameters. Experimental evaluations show that the combination of 2D representations on the different orthogonal views learned through the S3PNet is sufficient and effective for 3D representation, with the results outperforming current methods based on fully 3D CNNs. We support this with extensive evaluations on widely used 3D data sources in computer vision: CAD models, LiDAR point clouds, RGBD images, and 3D Computed Tomography scans. Experiments further demonstrate that S3PNet has better generalization capability for smaller training sets, and learns more of kernels with less redundancy compared to kernels learned from 3D CNNs.