Varying Spread Fuzzy Regression for Affective Quality Estimation

Abstract

Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state-of-art and the recently-developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria which address both predictions of magnitudes and fuzziness.

Color-Emotion Associations in Art: Fuzzy Approach

“…Emotion ambiguity and a few emotion classes compared to human feelings are some difficulties distinguishing emotions [42]. The fuzzy approach represents a valuable method for dealing with imprecision, and it has been utilized in several studies to represent emotions and their intensity in audio, speech, and video [43]- [46], color image [4], [8], [27]- [29], [47], text [48]- [51], face [44], [45], [52], [53] and product [54], [55] emotion analysis.…”

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

“…Therefore, IQA models are necessary to predict aesthetic qualities of captured images [13]. Our previous research developed the fuzzy regression approaches to generate IQA models [5,6]. The approaches attempt to overcome the limitation of the commonly used statistic regression and classical fuzzy regression, which cannot properly estimate uncertainty of IQA [5,6], but the approaches can only generates the linear fuzzy centers which are linearly correlated to the independent variables.…”

“…Our previous research developed the fuzzy regression approaches to generate IQA models [5,6]. The approaches attempt to overcome the limitation of the commonly used statistic regression and classical fuzzy regression, which cannot properly estimate uncertainty of IQA [5,6], but the approaches can only generates the linear fuzzy centers which are linearly correlated to the independent variables. Here the proposed NON-SC-FR is used to further enhance the robustness of IQA models, where the centers of the fuzzy estimates are generated by the genetic programming and they can be nonlinearly correlated to the independent variables.…”

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