Abstract
With the development of cognitive abilities of people, the human’s individual emotion would be very different styles in accordance with his environment and condition. Therefore, the human’s emotions to see and feel the same image can be different and this results in different images to see. In this paper, we construct a new style emotion model and propose a new emotion retrieval method from the image using the model. The emotion retrieval method in this research is composed of two phases. First, feature points to represent the image are extracted. Feature points are hue, saturation, frequency information, and circularity of image which represents the shape of the object. The extracted feature values are used as inputs of machine learning scheme in second phase. In machine learning scheme, learning and testing are performed with the typical three learning schemes. Through these machine learning schemes, the human’s emotion is extracted when to see a new image, and a new paradigm is provided to apply many different fields.
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References
Smeulders, A.W.M., et al.: Content-based Image Retrieval: the end of the early years. IEEE Trans. PAMI 22(12), 1349–1380 (2000)
Mojsilovic, A., Gomes, J., Rogowitz, B.: Semantic-Friendly Indexing and Querying of Images Based on the Extraction of the Objective Semantic Cues. Intl. J. of Computer Vision 56(1/3), 79–107 (2004)
Colombo, C., Bimbo, A.D., Pala, P.: Semantics in Visual Information Retrieval. IEEE Multimedia 6(3), 38–53 (1999)
Cho, S.B., Lee, J.Y.: A Human-Oriented Image Retrieval System Using Interactive Genetic Algorithm. IEEE Trans. SMC, Part A 32(3), 452–458 (2002)
Lee, J., et al.: Emotional Evaluation of Color Patterns Based on Rough Sets. In: Proc. 3th ICNC, vol. 1, pp. 140–144 (2007)
Li, J., Wang, J.Z.: Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach. IEEE Trans. PAMI 25(9), 1075–1088 (2003)
Olkiewicz, K.A., Markowska-Kaczmar, U.: Emotion-based Image Retrieval-an Artificial Neural Network Approach. In: Proc. International Multiconference on CSIT, pp. 89–96 (2010)
Lee, J., Zhang, L., Park, E.: An Emotion-based Image Retrieval System by Using Fuzzy Integral with Relevance Feedback. In: Proc. IEEK, vol. 31, pp. 683–688 (2008)
Wang, S.F.: Research on Emotion Information Processing and Its Application in Image Retrieval, Doctoral Dissertation, University of Science and Technology of China (2002)
Lee, J., et al.: Emotional Evaluation of Color Patterns Based on Rough Sets. In: Proc. 3th ICNC, vol. 1, pp. 140–144 (2007)
Yoo, H.W.: Visual-Based Emotional Descriptor and Feedback Mechanism for Image Retrieval. J. of Information Science and Engineering 22, 1205–1227 (2006)
Russell, J.: Pancultural Aspects of the Human Conceptural Organization of Emotions. J. of Personality and Social Psychology 45, 1281–1288 (1983)
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© 2012 Springer Science+Business Media Dordrecht
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Chang, JK., Ryoo, ST. (2012). Image-Based Emotion Retrieval Approach with Multi-machine Learning Schemes. In: Park, J., Leung, V., Wang, CL., Shon, T. (eds) Future Information Technology, Application, and Service. Lecture Notes in Electrical Engineering, vol 179. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5064-7_26
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DOI: https://doi.org/10.1007/978-94-007-5064-7_26
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5063-0
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