Image Composition and Semantic Expression of Traditional Folk Art Based on Evolutionary Computing Technology

Abstract

Image media are used by people to perceive the world’s material reality and spiritual symbols. Traditional folk art images, unlike natural scene images, are characterized by “form to write God.” Their semantic data are more abstract and detailed. As a result, folk art images limit the use of low-level visual feature descriptors in natural images. By simulating the evolution of natural species, evolutionary computation solves optimization problems. Black box optimization, combinatorial optimization, nonconvex optimization, and multiobjective optimization are all examples of optimization techniques. The semantic model is built using the semantic dictionary, and the semantic expression between images is then determined using semantic measurement. As a result, this paper focuses on the “semantic gap” of evolutionary computing-based image search technology, as well as related feedback and image semantic analysis technology. The results show that this method uses the deep database’s triple cross validation method to achieve average comfort and wakefulness, with accuracy of 63.62 percent and 72.38 percent, respectively, which is better than the methods in the comparative literature and can verify the algorithm’s efficiency and feasibility. To summarize, using evolutionary computing technology to improve the performance of object classification and retrieval, traditional folk art image composition and semantic expression can effectively reduce quantization error and improve the resolution of image semantic expression.