文献类型：会议论文

英文题名：Image Classification with a New Kind of Shape Representation

作者：Xu, Shaowu[1];Miao, Jun[1];Qing, Laiyun[2];Qiao, Yuanhua[3];Zou, Baixian[4]

第一作者：Xu, Shaowu

通讯作者：Miao, J[1]

机构：[1]Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing Key Lab Internet Culture & Digital Dissem, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China;[3]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China;[4]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China

第一机构：Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing Key Lab Internet Culture & Digital Dissem, Beijing 100101, Peoples R China

通讯机构：[1]corresponding author), Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing Key Lab Internet Culture & Digital Dissem, Beijing 100101, Peoples R China.

会议论文集：International Conference on Image and Video Processing, and Artificial Intelligence (IVPAI)

会议日期：AUG 15-17, 2018

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Shape Encoding; Image Classification; Shape Representation; CNN; Classification

摘要：As one of the research hotspots in recent years, especially in pattern recognition, Convolutional Neural Network (CNN) is widely known for its high efficiency. However some researches show that there is a problem in the CNN which cannot learn the high-level features. In order to solve this problem, this paper proposes a new kind of image representation, which we call it "shape encoding maps". Our experimental results show that, in most cases, the recognition accuracies obtained by inputting the shape encoded maps to a CNN are higher than that of using the original image data for a CNN to learn directly without shape encoding.