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Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system

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Abstract

Affective computing has various challenges especially for features extraction. Semantic information and vocal messages contain much emotional information, while extracting affective from features of images, and affective computing for image dataset are regarded as a promised research direction. This paper developed an improved adaptive neuro-fuzzy inference system (ANFIS) for images’ features extraction. Affective value of valence, arousal, and dominance were the proposed system outputs, where the color, morphology, and texture were the inputs. The least-square and k-mean clustering methods were employed as learning algorithms of the system. This improved model for structure and parameter identification of ANFIS were trained and validated. The training errors of the system for the affective values were tested and compared. Data sources grouping and the ANFIS generating processes were included. In the network training process, the number of input variables and fuzzy subset membership function types has been relative to network model under different affective inputs. Finally, well-established training model was used for testing using International Affective Picture System. The resulting network predicted those affective values, which compared to the expected outputs. The results demonstrated the effect of larger deviation of the individual data. In addition, the relationships between training errors, fuzzy sample set, training data set, function type, and the number of membership functions were illustrated. The proposed model showed the effectiveness for image affective extraction modeling with maximum training errors of 14 %.

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Acknowledgments

This work was sponsored by Zhejiang Provincial Natural Science Fund under Grant No. (LQ15A010009, Y17F030054) and National Natural Science Foundation of China under Grant No. (51205059).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People’s Republic of China

    Dan Wang & Zairan Li

  2. Wenzhou Vocational and Technical College, Wenzhou, 325035, People’s Republic of China

    Dan Wang & Zairan Li

  3. College of Information and Engineering, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China

    Ting He, Luying Cao, Yezhi Lin & Fuqian Shi

  4. Department of IT, Techno India College of Technology, Kolkata, West Bengal, 740000, India

    Nilanjan Dey

  5. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  6. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, 310130, Arad, Romania

    Valentina E. Balas

  7. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32825, USA

    Pamela McCauley

  8. College of Design and Innovation, Tongji University, Shanghai, 210000, People’s Republic of China

    Jiang Xu

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  1. Dan Wang
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Correspondence to Fuqian Shi.

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Wang, D., He, T., Li, Z. et al. Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system. Neural Comput & Applic 29, 1087–1102 (2018). https://doi.org/10.1007/s00521-016-2512-4

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