Machine learning for clothing manufacture as a mean to respond quicker and better to the demands of clothing brands: a Greek case study

Papachristou, Evridiki; Chrysopoulos, Antonios C.; Bilalis, Nikolaos

doi:10.1007/s00170-020-06157-1

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…There are now two different types of traditional ways for retrieving images of apparel. One is text-based image retrieval, which performs semantic matching on the text description of the clothing image [ 8 , 9 ]. The other type is image retrieval based on image content, which extracts features from the color, texture, and other aspects of the image.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Tao

Guo

2022

Journal of Environmental and Public Health

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The application of artificial intelligence (AI) technology in the field of clothes can provide a good development mode and system under the social context of AI technology development. AI provides help for the development of intelligent clothing. Intelligent clothing is a high-tech product that integrates intelligent technology and clothing. It combines cutting-edge technologies in electronic information technology, sensor technology, textile science, and material science. In the extraction and analysis of environmental factors in clothing handicraft, AI technology has a considerable application prospect and a certain development potential. In order to improve the accuracy of environmental factors extraction in clothing handicraft, this paper uses convolutional neural network (CNN) to extract and analyze environmental factors in traditional clothing handicraft. We carried out experiments on the extraction of environmental factors in clothing handicrafts with pure color, few patterns, patterns, and complex background. The experimental results show that the CNN has a good effect on the extraction of environmental factors in clothing handicraft under different backgrounds. In addition, the model in this paper has good stability, accuracy, and feature extraction speed, which has high practical value and research significance.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Tao

Guo

2022

Journal of Environmental and Public Health

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“…Moreover, the maximum dimension of the new tensor ≤ the maximum dimension of the original tensor, that is, I ' ≤I. Therefore, when N is relatively large, it is obvious that the time complexity of the proposed ITTD algorithm will be simpler and the total time consumed will be less [37][38] .…”

Section: Theoretical Analysis Of Ittd Algorithmmentioning

confidence: 99%

Data distribution and tensor influence analysis of different clustering methods

Zhang

Hao

et al. 2023

Preprint

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At present, people are in the era of big data, which is changing people's views of the world. However, it has the characteristics of various types, huge scale, and complex relationships. In order to solve the repeated calculation caused by streaming data in the processing of tensor-based big data, there will also be dimension disasters. Therefore, in this paper, an incremental tensor train decomposition (ITTD) method is proposed to solve multi-clustering problem in tensor-based big data analysis systems. It mainly uses results of the tensor train decomposition obtained from the original tensor to calculate and updates the results of tensor train decomposition to avoid the repetitive decomposition of the original tensor and enhance the decomposition efficiency. The performance of ITTD method is tested through theoretical analysis, a large number of simulation data and a comparative experiment on the real data of public transportation in a region. The experimental results indicate that the execution time of ITTD is significantly shorter than that of nonincremental tensor train decomposition(NTTD) with time. However, as time goes by, there is no obvious difference in the approximation error and storage space between the two.. This shows that, compared to that of the traditional nonincremental method, if the approximation error and storage space are close, the execution time of the incremental method will be greatly shortened. It can improve the processing efficiency of multi-clustering problems in the tensor-based big data analysis system.

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“…There are also some other classification methods, such as classifying clothing styles into Gothic, classical, romantic, Baroque, and other styles [ 4 ]. According to independent designer brands in different regions, clothing can also be divided into young playful, avant-garde, modern and simple, national retro, elegant, and romantic styles [ 5 ]. Thereby, the division of clothing style mainly depends on personal subjective judgment without unified definition standard [ 6 ].…”

Section: Introductionmentioning

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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Machine learning for clothing manufacture as a mean to respond quicker and better to the demands of clothing brands: a Greek case study

Cited by 19 publications

References 20 publications

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Data distribution and tensor influence analysis of different clustering methods

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

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