Title: Extraction and recognition of emotional features in artistic images using visual sensor network

Authors: Fei Liu

Addresses: College of Fine Arts and Design, Yangzhou University, Yangzhou 225009, China

Abstract: Artistic images serve as an important medium for emotional expression, and the precise identification of their emotional semantics is of great significance for art appreciation. To address the current issues of poor image quality and incomplete extraction of emotional features in artistic images, this paper first designs a multi-node wireless visual sensor network architecture to enable rapid acquisition of artistic images. Then, an improved Retina-Cortex algorithm is designed to enhance artistic images. Based on this, a feature extractor is constructed using a residual neural network to extract multidimensional emotional features, calculate the emotional weights of the three features, and perform feature fusion. Finally, the emotional features are used to identify the emotions in artistic images. The experimental results show that the average recognition accuracy and F1 score of the proposed model are 94.86% and 96.47%, enabling effective recognition of emotions in art images.

Keywords: visual sensor network; artistic images; feature extraction; emotion recognition; Retina-Cortex (Retinex) algorithm.

DOI: 10.1504/IJSNET.2025.149897

International Journal of Sensor Networks, 2025 Vol.49 No.3, pp.183 - 194

Received: 29 Jul 2025
Accepted: 19 Aug 2025
Published online: 17 Nov 2025 *

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