Adaptive-Tangent space representation for image retrieval based on kansei

Authors:
Myunggwon Hwang;Sunkyoung Baek;Hyunjang Kong;Juhyun Shin;Wonpil Kim;Soohyung Kim;Pankoo Kim
Affiliations:
Dept. of Computer Engineering, Chosun University, Gwangju, Korea;Dept. of Computer Engineering, Chosun University, Gwangju, Korea;Dept. of Computer Engineering, Chosun University, Gwangju, Korea;Dept. of Computer Engineering, Chosun University, Gwangju, Korea;Dept. of Computer Science, Chonnam National University, Gwangju, Korea;Dept. of Computer Science, Chonnam National University, Gwangju, Korea;Dept. of Computer Engineering, Chosun University, Gwangju, Korea
Venue:
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Year:
2006

Citing 4
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Abstract

From the engineering aspect, the research on Kansei information is a field aimed at processing and understanding how human intelligence processes subjective information or ambiguous sensibility and how such information can be executed by a computer. Our study presents a method of image processing aimed at accurate image retrieval based on human Kansei. We created the Kansei-Vocabulary Scale by associating Kansei of high-level information with shapes among low-level features of an image and constructed the object retrieval system using Kansei-Vocabulary Scale. In the experimental process, we put forward an adaptive method of measuring similarity that is appropriate for Kansei-based image retrieval. We call it “adaptive-Tangent Space Representation (adaptive-TSR)”. The method is based on the improvement of the TSR in 2-dimensional space for Kansei-based retrieval. We then it define an adaptive similarity algorithm and apply to the Kansei-based image retrieval. As a result, we could get more promising results than the existing method in terms of human Kansei.