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.
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Hwang, M. et al. (2006). Adaptive-Tangent Space Representation for Image Retrieval Based on Kansei. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_79
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DOI: https://doi.org/10.1007/11925231_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
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