Texture-Based Features for Clothing Classification via Graph-Based Representation

Thewsuwan, Srisupang; Horio, Keiichi

doi:10.2299/jsp.22.299

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…The local graph structure has been used for face recognition tasks [220], [222]. Another graph-based approach has been used for clothing classification [223].…”

Section: ) Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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“…The local graph structure has been used for face recognition tasks [220], [222]. Another graph-based approach has been used for clothing classification [223].…”

Section: ) Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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“…In the simplest case of only checking the closest neighbors (radius r=1) we obtain an 8-bit binary number and convert it to decimal. All decimal numbers of the image are combined into a Over the years various modifications to the original LBP were proposed, including multiscale extension [42], spatial enhancement [43], and texture uniformity classification [44]. These methods often include concatenation of several local histograms to encode additional spatial information.…”

Section: Texture Similaritymentioning

confidence: 99%

Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification

Gabdullin¹

2022

Preprint

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Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning and particularly Deep Learning (DL) has become the main re-id tool that allowed researches to achieve unprecedented accuracy levels on benchmark datasets. However, there is a known problem of poor generalization of DL models. That is, models trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training. To address this issue, we present a model without trainable parameters which shows great potential for high generalization. It combines a fully analytical feature extraction and similarity ranking scheme with DL-based human parsing used to obtain the initial subregion classification. We show that such combination to a high extent eliminates the drawbacks of existing analytical methods. We use interpretable color and texture features which have humanreadable similarity measures associated with them. To verify the proposed method we conduct experiments on Market1501 and CUHK03 datasets achieving competitive rank-1 accuracy comparable with that of DL-models. Most importantly we show that our method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to transfer learning tasks. It is significantly higher than previously reported 30-50% transfer accuracy. We discuss the potential ways of adding new features to further improve the model. We also show the advantage of interpretable features for constructing human-generated queries from verbal description to conduct search without a query image.

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“…Compared with CNN, LeNet, Long Short-Term Memory network (LSTM), and Bidirectional LSTM (BiLSTM) models, the experimental results of these two methods had certain advantages. However, the classification accuracy of Thewsuwan and Horio's (2018) work is still not high enough, at only almost 88 percent. The method of Li et al (2019) is an experiment based on a large-scale image network.…”

Section: Introductionmentioning

confidence: 97%

“…The application of AI to fashion clothing is an exciting research field. For example, they are using deep learning (DL) to perceive and detect fashion clothing materials, clothing image recognition, and classification (Thewsuwan & Horio, 2018;Li et al, 2019). Fashion clothing has always been essential to people's pursuit of life taste.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the research on fashion clothing image classification, Thewsuwan and Horio (2018) proposed the realization of clothing classification based on texture features utilizing graph-based representation. The experiment was performed by constructing texture and clothing feature recognizers based on three datasets, which included Brodatz texture, UIUC standard texture image database, and the clothing image dataset.…”

Section: Introductionmentioning

confidence: 99%

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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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Texture-Based Features for Clothing Classification via Graph-Based Representation

Cited by 9 publications

References 11 publications

Texture Feature Extraction Methods: A Survey

Texture Feature Extraction Methods: A Survey

Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification

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

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