Spatial relationship representation for visual object searching

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 memor...

• Type: Journal
• Year: 2008
• Where: IJON
• Authors: Jun Miao, Lijuan Duan, Laiyun Qing, Wen Gao, Xilin Chen, Yuan Yuan
• Keywords: IJON 2008, Key Issue, Sparse Coding, Spatial Relationship