Visual Texture Perception with Feature Learning Models and Deep Architectures

摘要：

　　Texture is an important property of images,and a key component for human visual perception.In this work,based on several feature learning models and deep architectures,we study the visual texture perception problem,which is helpful for understanding both the impact of texture itself and the basic mechanisms of human visual systems.Through a series of psychophysical experiments,we find that 12 perceptual features are significant to describe the texture images with regard to the human perceptions.Hence,we represent each texture image with a 12-dimensional vector,corresponding to the values of the 12 perceptual features.To improve the learnablity of existing feature learning models,we propose a set of deep architectures to learn compact representations of the texture perceptual features.Extensive experiments on texture images classification demonstrate the effectiveness of both the feature learning models and the deep architectures.In particular,the advantage of deep architectures over existing feature learning models is shown.

关键词： Texture visual perception feature learning deep architectures

作者: Yuchen Zheng Guoqiang Zhong Jun Liu Xiaoxu Cai Junyu Dong

作者单位: Department of Computer Science and Technology,Ocean University of China,238 Songling Road,Qingdao,China 266100

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 401-410

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）