Application of Multitask Joint Sparse Representation Algorithm in Chinese Painting Image Classification

Abstract

This paper presents an in-depth study and analysis of Chinese painting image classification by a multitask joint sparse representation algorithm for texture feature extraction of Chinese painting images and proposes a method to extract texture features directly for the original images. It simplifies the process of image grayscale conversion and preserves the information contained in the original Chinese painting images to the greatest extent. The algorithm uses the ideas of multicolor domain analysis and multiscale analysis, combined with the traditional grayscale coeval matrix to extract texture features. Experiments show that the multiscale grayscale cooccurrence matrix algorithm outperforms the traditional grayscale cooccurrence matrix algorithm and the color grayscale cooccurrence matrix algorithm. The discriminative ability of multiple features for target recognition is integrated by multitask learning, thus improving the robustness and generalization ability of the algorithm; meanwhile, the recognition accuracy is improved by using a two-level multitask learning mode to exclude the interference of a large number of irrelevant dictionary atoms. The experimental results show that the algorithm has higher recognition accuracy and better robustness than the existing sparse representation SAR target recognition algorithm. Configuration recognition experiments are conducted on different configurations of target data, and the experimental results show that the algorithm achieves better configuration recognition accuracy than existing algorithms.

Fusion Analysis of Chinese Painting Color Teaching and Intelligent Image Color Processing Technology

“…e research on low-level feature extraction, data compression, automatic semantic annotation, retrieval, and automatic classification of digital Chinese painting image is more and more extensive [6]. Yang et al [7] found that the research on automatic classification and annotation of digital images of Chinese painting involves the integration of computer vision, machine learning, image retrieval, cognitive psychology, and painting art. In general, because there is no direct relationship between the low-level visual features and high-level semantic concepts of Chinese painting images, the automatic semantic classification and annotation of Chinese painting images is a very challenging research topic [7].…”

“…Yang et al [7] found that the research on automatic classification and annotation of digital images of Chinese painting involves the integration of computer vision, machine learning, image retrieval, cognitive psychology, and painting art. In general, because there is no direct relationship between the low-level visual features and high-level semantic concepts of Chinese painting images, the automatic semantic classification and annotation of Chinese painting images is a very challenging research topic [7]. Li et al [8] have studied content-based image retrieval (CBIR) and image classification technology for more than ten years.…”

“…(1) Uniform Monotonicity: as shown in formula (7) (2) Progressive Completeness: as shown in formula (8) (3) Scaling Regularity: as shown in formula (9) (4) Translation Invariance: as shown in formula (10)…”

Analysis of the Impact of Artificial Intelligence Technology‐Assisted Environmental Protection on the Integrity of Chinese Painting

“…These harmful substances will accelerate the damage and destruction of Chinese paintings. Sulfur substances and harmful factors in the air will accelerate the damage to Chinese paintings [ 7 , 8 ]. For Han painting, it mainly shows the contemporary economic and political development trend through the patterns, colors, and painting forms of Han painting.…”

Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing

“…In this way, they can not only express their feelings in the works, but also make the images have emotion and vitality. Emotion is the driving force of Chinese painting creation (Bi et al, 2019;Yang et al, 2021). Traditional algorithms to recognize human affects in Chinese paintings often combine art theory and computer vision, practicing artificially designed features and statistical ML (Machine Learning) approaches to recognize the emotional responses evoked by Chinese paintings.…”