Fusion Mode and Style Based on Artificial Intelligence and Clothing Design

Qiu, Jiali; Ma, Lianghua

doi:10.1155/2021/6293539

Cited by 20 publications

(14 citation statements)

References 20 publications

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“…Pruning is a basic technology to overcome the data noise of the training sample set. When pruning and optimizing the tree, it is necessary to accurately understand the classification feature description and prevent too much noise to achieve a better pruning effect and improve comprehensibility while ensuring accuracy [ 23 , 24 ]. A decision tree-based classification algorithm is widely used because of its unique advantages.…”

Section: Methodsmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

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Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.

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Section: Methodsmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

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“…Ecolabels can motivate designers to consider environmental protection and sustainability factors in material selection, production processes, and product life, thus promoting the sustainable development of the apparel industry. In the literature [16], specific methods and strategies for apparel design using artificial intelligence technology were introduced, and the prospects for its use in apparel design were explored. According to the research, AI can bring more innovation and diversity to apparel design and aid and promote the intelligent and digital transformation of the apparel industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of 3D visual perception-driven design of velvet flowers in traditional dresses

Fan

2023

Applied Mathematics and Nonlinear Sciences

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This paper firstly analyzes the specific application of velvet flowers on women’s dresses from the perspective of fashion aesthetics and with the understanding of the background and development trend of dresses. Secondly, based on 3D visual perception, we propose the optimization of the normalization method and loss function optimization, which can improve the migration speed and generation quality of velvet flower design, and then seamlessly fit it to the dress base map by Poisson fusion to realize the intelligent design of velvet flower in dresses driven by 3D visual perception. Then, the color features and attributes of the floral in the dress are studied, and the HSV color space model is used to extract the high-frequency colors and attributes of the dress. Then, the floral design is digitized, and the dress design is driven by 3D visual perception is studied and analyzed. The results show that the loss value of the dress design style migration network trained based on AdaN is the smallest, and the loss value is reduced by 0.19 compared to the dress design style migration network using IN and 0.31 compared to BN. The velvet flower design on the dress design base map can be implemented based on the foundation provided by this study.

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“…AI is changing every field of life and the same is true for the garments industry. In the present study, Qiu and Ma 10 have analyzed the styles and structure of cloth design in the light of AI techniques. Based on the comparative experiment, the present study focuses on digital clothing design.…”

Section: Related Workmentioning

confidence: 99%

Ranking the art design and applications of artificial intelligence and machine learning

Nazir

2022

J Software Evolu Process

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Art design is a method of conveying human feelings and emotions, particularly via the use of visual structures such as paintings or sketches. Every element of our life, including arts and crafts, has been positively affected by the introduction of novel technologies such as artificial intelligence (AI) and machine learning (ML). In today's modern environment, these technologies have altered the techniques of art creation, consumption, and distribution. In today's environment, ML and human emotions are the two most important aspects for interactive and high‐quality art design. Whereas traditional learning systems can be extremely effective in the teaching and learning process of art‐related subjects, AI and ML can be very effective in the teaching and learning process of art‐related subjects for the advancement of learners' artistic skills. The productive and active role of AI and ML in the developments and advancements of art design has been given a very comprehensive and detailed overview in this study. Following a detailed examination of the existing techniques, distinct characteristics have been identified. Six of the most widely utilized features were chosen from the literature to execute the analytical hierarchy process (AHP). For ranking the options based on the weights derived by AHP, the TOPSIS algorithm is used. The option with the best performance came in first, whereas the one with the worst performance came in last.

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Fusion Mode and Style Based on Artificial Intelligence and Clothing Design

Cited by 20 publications

References 20 publications

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Application of 3D visual perception-driven design of velvet flowers in traditional dresses

Ranking the art design and applications of artificial intelligence and machine learning

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