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Emotion Image Retrieval through Query Emotion Descriptor and Relevance Feedback  

Yoo Hun-Woo (연세대학교 인지과학연구소)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.3, 2005 , pp. 141-152 More about this Journal
Abstract
A new emotion-based image retrieval method is proposed in this paper. Query emotion descriptors called query color code and query gray code are designed based on the human evaluation on 13 emotions('like', 'beautiful', 'natural', 'dynamic', 'warm', 'gay', 'cheerful', 'unstable', 'light' 'strong', 'gaudy' 'hard', 'heavy') when 30 random patterns with different color, intensity, and dot sizes are presented. For emotion image retrieval, once a query emotion is selected, associated query color code and query gray code are selected. Next, DB color code and DB gray code that capture color and, intensify and dot size are extracted in each database image and a matching process between two color codes and between two gray codes are peformed to retrieve relevant emotion images. Also, a new relevance feedback method is proposed. The method incorporates human intention in the retrieval process by dynamically updating weights of the query and DB color codes and weights of an intra query color code. For the experiments over 450 images, the number of positive images was higher than that of negative images at the initial query and increased according to the relevance feedback.
Keywords
Emotion-based image retrieval; Color Code; Gray Code; Relevance Feedback; Weight Update;
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