Title: Emotion recognition of digital art image based on weighted fusion strategy

Authors: Xin Meng; Huili Tan; Xinyue Zhang

Addresses: Department of Experimental Art, LuXun Academy of Fine Arts, Shenyang, 110004, China ' Department of Experimental Art, LuXun Academy of Fine Arts, Shenyang, 110004, China ' Department of Textile and Fashion Design, LuXun Academy of Fine Arts, Shenyang, 110004, China

Abstract: In order to overcome the problems of low recognition accuracy and low recognition efficiency of traditional image emotion recognition methods, this paper proposes a digital art image emotion recognition method based on weighted fusion strategy. First, image emotional tags are designed, and image samples are selected using information entropy. Secondly, Gaussian fuzzy is used to reduce image noise and extract image emotional features. Then, the weighted fusion strategy is used to construct a weighted matrix to determine the similarity between feature classes; Finally, SVM classifier is constructed to classify image emotion features, emotion recognition function is designed according to weighted fusion strategy, and emotion recognition result is solved according to maximum rule. The results show that the recognition time of this method is less than 30 s, and the recognition accuracy can reach 99.0%, which shows that this method can improve the effect of emotion recognition.

Keywords: weighted fusion; emotion recognition; Laplace operator; CLAHE algorithm; maximum rule.

DOI: 10.1504/IJRIS.2024.139836

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.3, pp.206 - 214

Received: 07 Dec 2022
Accepted: 14 Mar 2023
Published online: 08 Jul 2024 *

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