Spatial relationship representation for visual object searching

Authors:
Jun Miao;Lijuan Duan;Laiyun Qing;Wen Gao;Xilin Chen;Yuan Yuan
Affiliations:
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;College of Computer Science and Technology, Beijing University of Technology, Beijing 100022, China;School of Information Science and Engineering, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Institute for Digital Media, Peking University, Beij ...;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UK
Venue:
Neurocomputing
Year:
2008

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Abstract

Image representation has been a key issue in vision research for many years. In order to represent various local image patterns or objects effectively, it is important to study the spatial relationship among these objects, especially for the purpose of searching the specific object among them. Psychological experiments have supported the hypothesis that humans cognize the world using visual context or object spatial relationship. How to efficiently learn and memorize such knowledge is a key issue that should be studied. This paper proposes a new type of neural network for learning and memorizing object spatial relationship by means of sparse coding. A group of comparison experiments for visual object searching between several sparse features are carried out to examine the proposed approach. The efficiency of sparse coding of the spatial relationship is analyzed and discussed. Theoretical and experimental results indicate that the newly developed neural network can well learn and memorize object spatial relationship and simultaneously the visual context learning and memorizing have certainly become a grand challenge in simulating the human vision system.