Context‐aware encoding for clothing parsing

Yoo, Cheol-Hwan; Shin, Yong-Goo; Kim, Seung Wook; Ko, Sung-Jea

doi:10.1049/el.2019.1213

Cited by 4 publications

(6 citation statements)

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“…Some of the initial approaches focused on extending side branch on Fully Convolutional Network (FCN), such as Outfit Encoder (OE) side branch (Tangseng et al. , 2017), Context-aware Outfit Encoder (COE) based on OE (Yoo et al. , 2019), texture stream (Khurana et al.…”

Section: Related Workmentioning

confidence: 99%

“…Both OE (Tangseng et al. , 2017) and COE (Yoo et al. , 2019) make use of clothing combination information to improve clothing parsing, but they only consider information of current input image.…”

Section: Related Workmentioning

confidence: 99%

“…Later, with the successful application of deep learning in computer vision, researchers started to use deep learning techniques for semantic segmentation of clothing images. Some of the initial approaches focused on extending side branch on Fully Convolutional Network (FCN), such as Outfit Encoder (OE) side branch (Tangseng et al, 2017), Context-aware Outfit Encoder (COE) based on OE (Yoo et al, 2019), texture stream (Khurana et al, 2018), head parsing self-network and body parsing sub-network (Zhou et al, 2019), etc. Both OE (Tangseng et al, 2017) and COE (Yoo et al, 2019) make use of clothing combination information to improve clothing parsing, but they only consider information of current input image.…”

Section: Related Work 21 Clothing Parsingmentioning

confidence: 99%

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OMNet: Outfit Memory Net for clothing parsing

Wang

Chen

et al. 2023

IJCST

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PurposeExisting clothing parsing methods make little use of dataset-level information. This paper aims to propose a novel clothing parsing method which utilizes higher-level outfit combinatorial consistency knowledge from the whole clothing dataset to improve the accuracy of segmenting clothing images.Design/methodology/approachIn this paper, the authors propose an Outfit Memory Net (OMNet) that augments original feature by aggregating dataset-level prior clothing combination information. Specifically, the authors design an Outfit Matrix (OM) to represent clothing combination information of single image and an Outfit Memory Module (OMM) to store the clothing combination information of all images in the training set, i.e. dataset-level clothing combination information. In addition, the authors propose a Multi-scale Aggregation Module (MAM) to aggregate the clothing combination information in a multi-scale manner to solve the problem of large variance in the scale of objects in the clothing images.FindingsExperiments on Colorful Fashion Parsing Dataset (CFPD) dataset show that the authors' method achieves 93.15% pixel accuracy (PA) and 51.24% mean of class-wise intersection over union (mIoU), which are satisfactory parsing results compared with existing methods such as PSPNet, DANet and DeepLabV3. Moreover, through comparing the segmentation accuracy of different methods for each category, MAM could effectively improve the segmentation of small objects.Originality/valueWith the rise of various online shopping platforms and the continuous development of deep learning technology, emerging applications such as clothing recommendation, matching, classification and virtual try-on system have emerged in the clothing field. Clothing parsing is the key technology to realize these applications. Therefore, improving the accuracy of clothing parsing is necessary.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Work 21 Clothing Parsingmentioning

confidence: 99%

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OMNet: Outfit Memory Net for clothing parsing

Wang

Chen

et al. 2023

IJCST

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“…The segmentation of clothes in images, also known as clothing parsing [15], consists of classifying each image pixel with labels specifically related to clothes (and accessories). Currently, it is still a challenging topic and has aroused the interest among researchers due to the inexhaustible variety of types, styles, colors, and shapes of clothes [16].…”

Section: Introductionmentioning

confidence: 99%

EPYNET: Efficient Pyramidal Network for Clothing Segmentation

Inácio

Lopes

2020

IEEE Access

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Soft biometrics traits extracted from a human body, including the type of clothes, hair color, and accessories, are useful information used for people tracking and identification. Semantic segmentation of these traits from images is still a challenge for researchers because of the huge variety of clothing styles, layering, shapes, and colors. To tackle these issues, we proposed EPYNET, a framework for clothing segmentation. EPYNET is based on the Single Shot MultiBox Detector (SSD) and the Feature Pyramid Network (FPN) with the EfficientNet model as the backbone. The framework also integrates data augmentation methods and noise reduction techniques to increase the accuracy of the segmentation. We also propose a new dataset named UTFPR-SBD3, consisting of 4,500 manually annotated images into 18 classes of objects, plus the background. Unlike available public datasets with imbalanced class distributions, the UTFPR-SBD3 has, at least, 100 instances per class to minimize the training difficulty of deep learning models. We introduced a new measure of dataset imbalance, motivated by the difficulty in comparing different datasets for clothing segmentation. With such a measure, it is possible to detect the influence of the background, classes with small items, or classes with a too high or too low number of instances. Experimental results on UTFPR-SBD3 show the effectiveness of EPYNET, outperforming the state-of-art methods for clothing segmentation on public datasets. Based on these results, we believe that the proposed approach can be potentially useful for many real-world applications related to soft biometrics, people surveillance, image description, clothes recommendation, and others.

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“…Extracting clothing masks is useful in a variety of applications such as automatic product tagging, virtual try-on and style synthesis, leading to an enhanced customer experience through better product suggestion and visualisation. Existing works have already focused on clothing segmentation and, in terms of other dense labelling tasks, convolutional neural networks (CNNs) provide state-of-the-art performance [1,2]. These methods rely on RGB images solely.…”

mentioning

confidence: 99%

Multi‐modal deep network for RGB‐D segmentation of clothes

Joukovsky

Munteanu

2020

Electron. lett.

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In this Letter, the authors propose a deep learning based method to perform semantic segmentation of clothes from RGB-D images of people. First, they present a synthetic dataset containing more than 50,000 RGB-D samples of characters in different clothing styles, featuring various poses and environments for a total of nine semantic classes. The proposed data generation pipeline allows for fast production of RGB, depth images and ground-truth label maps. Secondly, a novel multi-modal encoder-ecoder convolutional network is proposed which operates on RGB and depth modalities. Multi-modal features are merged using trained fusion modules which use multi-scale atrous convolutions in the fusion process. The method is numerically evaluated on synthetic data and visually assessed on real-world data. The experiments demonstrate the efficiency of the proposed model over existing methods.

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Context‐aware encoding for clothing parsing

Cited by 4 publications

References 5 publications

OMNet: Outfit Memory Net for clothing parsing

OMNet: Outfit Memory Net for clothing parsing

EPYNET: Efficient Pyramidal Network for Clothing Segmentation

Multi‐modal deep network for RGB‐D segmentation of clothes

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