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Illustration visual communication based on computer vision image retrieval algorithm
H.Z. Zhang 1

1 School of Art and Design, Minnan Science and Technology College,
362000, China, Kangmei Town, Quanzhou, Kangyuan Road, No. 8

 PDF, 1894 kB

DOI: 10.18287/2412-6179-CO-1449

Pages: 132-140.

Full text of article: English language.

Abstract:
In illustration design, good visual communication can make the audience resonate. Computer vision image retrieval algorithm provides important support and assistance for the visual communication of illustration. However, the traditional image retrieval algorithm has problems of subjectivity and inaccuracy in complex image classification. Therefore, this paper optimizes the feature extraction module of convolutional neural network and fuses hash algorithm to improve the efficiency and speed of image retrieval. The experimental results show that the accuracy of the improved convolutional neural network is 82.7 %, which is more than 6 percentage points higher than the traditional algorithm model. The recall rate of the volume neural network model improved by hashing algorithm is 94.1 %. Research is of great significance to the visual communication of illustration design, which helps designers to find relevant materials more accurately, improve the artistic quality and ornamental value of their works, and promote the innovation and development of illustration design.

Keywords:
visual communication; image retrieval; convolutional neural network; Hash algorithm.

Citation:
Zhang HZ. Illustration visual communication based on computer vision image retrieval algorithm. Computer Optics 2025; 49 (1): 132-140. DOI: 10.18287/2412-6179-CO-1449.

Acknowledgements:
The research was supported by the Fujian Provincial Department of Education’s 2017 Young and Middle-aged Teacher Education Research Project (Social Sciences) “Research on the Combination of Chinese and Western Architectural Decoration in Modern Overseas Chinese Residential Buildings in Quanzhou” (Project No.: JAS170827).

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