Indexed by:会议论文

Date of Publication:2018-01-01

Included Journals:CPCI-S

Volume:11164

Page Number:448-457

Key Words:Robust Gaussian descriptor; Second-order statistics; Convolutional neural network; Texture recognition

Abstract:Recently, second-order statistical modeling methods with convolutional features have shown impressive potential as image representation for vision tasks. Among them, bilinear convolutional neural network (B-CNN) has attracted a lot of attentions due to its simplicity and effectiveness. It captures the second-order local feature statistics via outer product, which approximately explores the covariance between convolutional features and achieves promising performance for texture recognition. In order to inherit the merits of B-CNN while further improving its performance, we introduce a Gaussian descriptor into B-CNN and propose a novel robust deep Gaussian descriptor (RDGD) method for texture recognition. We first compute Gaussian by using the output of outer product of B-CNN, and then embed it into the space of symmetric positive definite (SPD) matrices. Finally, matrix power normalization operation is employed to obtain more robust Gaussian descriptor. Experimental results on three texture databases demonstrate that RDGD is superior to its baseline B-CNN and the state-of-the-arts.