Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats

Medina, Adán; Méndez, Juana Isabel; Ponce, Pedro; Peffer, Therese; Meier, Alan; Molina, Arturo

doi:10.3390/en15051811

Cited by 20 publications

(4 citation statements)

References 37 publications

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“…This paper focuses mainly on optimizing model structure and learning rate. In addition, various studies (Medina et al, 2022;Ma et al, 2022;Vijayaraj et al, 2022), such as deep learning, recognition, and classification for fashion clothing images are currently being conducted.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Fashion Clothing Image Classification Based on Deep Learning

Shin

Jo²,

Wang

2023

JICT

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Image recognition and classification is a significant research topic in computational vision and widely used computer technology. Themethods often used in image classification and recognition tasks are based on deep learning, like Convolutional Neural Networks(CNNs), LeNet, and Long Short-Term Memory networks (LSTM). Unfortunately, the classification accuracy of these methods isunsatisfactory. In recent years, using large-scale deep learning networks to achieve image recognition and classification canimprove classification accuracy, such as VGG16 and Residual Network (ResNet). However, due to the deep network hierarchyand complex parameter settings, these models take more time in the training phase, especially when the sample number is small, which can easily lead to overfitting. This paper suggested a deep learning-based image classification technique based on a CNN model and improved convolutional and pooling layers. Furthermore, the study adopted the approximate dynamic learning rate update algorithm in the model training to realize the learning rate’s self-adaptation, ensure the model’s rapid convergence, and shorten the training time. Using the proposed model, an experiment was conducted on the Fashion-MNIST dataset, taking 6,000 images as the training dataset and 1,000 images as the testing dataset. In actual experiments, the classification accuracy of the suggested method was 93 percent, 4.6 percent higher than that of the basic CNN model. Simultaneously, the study compared the influence of the batch size of model training on classification accuracy. Experimental outcomes showed this model is very generalized in fashion clothing image classification tasks.

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Section: Related Workmentioning

confidence: 99%

A Novel Method for Fashion Clothing Image Classification Based on Deep Learning

Shin

Jo²,

Wang

2023

JICT

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“…Accurate identification or classification of clothing categories serves as a fundamental prerequisite for quickly accessing clothes of a similar style. This aspect holds significant value in the expanding fashion industry, where monitoring and categorizing commercial clothing are crucial (Medina et al, 2022). However, the commercial market encompasses a wide range of clothing products, including renowned brands and mediumsized personalized brands that cater to diverse consumer preferences.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

Huang

2023

IJICC

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PurposeSingle-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.Design/methodology/approachTo address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.FindingsEmpirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/valueThis paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

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“…In [ 39 ], a CNN model was proposed for clothing classification, where the algorithms YOLOv3 and Tiny YOLO were analyzed. Furthermore, a learning framework was proposed for automatically classifying clothing genres based on the visually differentiable style elements [ 40 ].…”

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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Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats

Cited by 20 publications

References 37 publications

A Novel Method for Fashion Clothing Image Classification Based on Deep Learning

A Novel Method for Fashion Clothing Image Classification Based on Deep Learning

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

Deep Learning for Clothing Style Recognition Using YOLOv5

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