TY - GEN
T1 - Emotional color features for natural image analysis
AU - Phongsuphap, Sukanya
AU - Kong-Am, Jinnawat
AU - Utanon, Wimon
N1 - Publisher Copyright:
©2013 IEEE.
PY - 2013
Y1 - 2013
N2 - This paper proposes a new set of image features called emotional color features. They are derived from the relationship between color and emotion. The procedure is as follows. An input color image is transformed from RGB to L∗a∗b∗ and L∗C∗h color models. Then, the color data are divided into 20 groups of emotional color using the CIE delta E measurement-based color quantization. After that, emotional color features of the input image are calculated from the histogram of the 20 emotional colors. We have applied the proposed features to content-based image retrieval problem in the natural scene image domain. We found that the retrieval results were consistent with users' color emotional response. The average consistency score of the top ten image retrieval results using the proposed features was at a high level. The proposed features performed better than the traditional color histogram features. In addition, the emotional color features have the desired properties such as size of feature vector is short, and computation is not complex, which are useful for searching huge image databases.
AB - This paper proposes a new set of image features called emotional color features. They are derived from the relationship between color and emotion. The procedure is as follows. An input color image is transformed from RGB to L∗a∗b∗ and L∗C∗h color models. Then, the color data are divided into 20 groups of emotional color using the CIE delta E measurement-based color quantization. After that, emotional color features of the input image are calculated from the histogram of the 20 emotional colors. We have applied the proposed features to content-based image retrieval problem in the natural scene image domain. We found that the retrieval results were consistent with users' color emotional response. The average consistency score of the top ten image retrieval results using the proposed features was at a high level. The proposed features performed better than the traditional color histogram features. In addition, the emotional color features have the desired properties such as size of feature vector is short, and computation is not complex, which are useful for searching huge image databases.
KW - Affective features
KW - Color features
KW - Color image analysis
KW - Color image retrieval
KW - Emotional color
UR - https://www.scopus.com/pages/publications/84941084838
U2 - 10.1109/KST.2013.6512794
DO - 10.1109/KST.2013.6512794
M3 - Conference contribution
AN - SCOPUS:84941084838
T3 - Proceedings of the 2013 5th International Conference on Knowledge and Smart Technology, KST 2013
SP - 92
EP - 95
BT - Proceedings of the 2013 5th International Conference on Knowledge and Smart Technology, KST 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Knowledge and Smart Technology, KST 2013
Y2 - 31 January 2013 through 1 February 2013
ER -