Clothing fashion style recognition with design issue graph

Yue, Xiaodong; Zhang, Cheng; Fujita, Hamido; Lv, Yiliang

doi:10.1007/s10489-020-01950-7

Cited by 17 publications

(10 citation statements)

References 48 publications

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“…Identifying clothing style based on local semantic features means that style classification is sensitive to the appearance of clothing items. To address this issue, Yue et al [63] developed design issue graphs (DIGs) to provide global and semantic descriptions of clothing styles. However, the precise definition of fashion style remains an ongoing research problem.…”

Section: Fashion Style Learningmentioning

confidence: 99%

Fashion Intelligence in the Metaverse: Promise and Future Prospects

Zhang

Shi

et al. 2023

Preprint

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With the development of artificial intelligence (AI) and the constraints on offline activities imposed due to the sudden outbreak of the COVID epidemic, the Metaverse has recently attracted significant research attention from both academia and industrial practitioners. Fashion, as an expression of a consumer’s aesthetics and personality, has enormous economic potential in both the real world and the Metaverse. In this research, we provide a comprehensive survey of two of the most important components of fashion in the Metaverse: virtual digital humans, and tasks related to fashion items. We survey state-of-the-art articles from 2007 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the applications of these topics in the Metaverse from the perspectives of designers and consumers. Finally, we describe possible scenes involving fashion in the Metaverse. The current challenges and open issues related to the fashion industry in the Metaverse are also discussed in order to provide guidance for fashion practitioners, and to shed some light on the future development of fashion AI in the Metaverse.

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Section: Fashion Style Learningmentioning

confidence: 99%

Fashion Intelligence in the Metaverse: Promise and Future Prospects

Zhang

Shi

et al. 2023

Preprint

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“…In [ 35 ], the R-CNN network framework combined with Softmax was applied for extracting features of shirt images, and the results indicated that an accuracy of 73.59% and a recall rate of 83.84% can be attained. In the research of clothing recognition using deep learning techniques, DeepFashion and DeepFashion2 are two datasets that have attracted lots of attention [ 36 , 37 , 38 ]. For example, fashion style recognition can help e-commerce clothing retrieval and recommendation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, fashion style recognition can help e-commerce clothing retrieval and recommendation. In order to solve the problem of classification errors caused by the same style of clothing images in different visual appearances, a joint fashion style recognition model was proposed, which was verified using the DeepFashion dataset [ 37 ]. In practice, it is necessary to establish the target object before performing garment inspection.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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“…Pure clothing images are simply displayed clothing without a human model, with a large solid background, such as clothing flat display images. Classifying pure clothing images is relatively easy but still has a low discriminability across different textures, colours, and fabric features [7]. In contrast, dressed clothing images often have a large portion of the complex background with a human model, for example, "seller's show" and "buyer's show."…”

Section: Introductionmentioning

confidence: 99%

Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models

Wei

Wang

et al. 2022

Fibres &Amp; Textiles in Eastern Europe

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Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups: pure clothing images and dressed clothing images. Targeting small and medium-sized clothing companies or merchants, it compares traditional machine learning and deep learning models to determine suitable models for each group. For pure clothing images, the HOG+SVM algorithm with the Gaussian kernel function obtains the highest classification accuracy of 91.32% as compared to the Small VGG network. For dressed clothing images, the CNN model obtains a higher accuracy than the HOG+SVM algorithm, with the highest accuracy rate of 69.78% for the Small VGG network. Therefore, for end-users with only ordinary computing processors, it is recommended to apply the traditional machine learning algorithm HOG+SVM to classify pure clothing images. The classification of dressed clothing images is performed using a more efficient and less computationally intensive lightweight model, such as the Small VGG network.

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Clothing fashion style recognition with design issue graph

Cited by 17 publications

References 48 publications

Fashion Intelligence in the Metaverse: Promise and Future Prospects

Fashion Intelligence in the Metaverse: Promise and Future Prospects

Deep Learning for Clothing Style Recognition Using YOLOv5

Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models

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