Parsing clothing in fashion photographs

Yamaguchi, Kota; Kiapour, M. Hadi; Ortiz, Luis E.; Berg, Tamara L.

doi:10.1109/cvpr.2012.6248101

Cited by 431 publications

(495 citation statements)

References 21 publications

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“…Because the main datasets for the analysis of clothes are composed by images extracted by TV series where there is a lack of variability. Moreover other datasets in this particular domain suffers of main problems, as an example the most recent dataset in this field [25] consists only of images of women.…”

Section: Methodsmentioning

confidence: 99%

“…This approach obtained good results but is able to extract only a small set of generic visual attributes. In [25] they use for the first time the information related with the human pose to segment different part of the body in order to extract the Visual Information related with the clothing. This information is particularly useful to classify the type of clothes.…”

Section: Introductionmentioning

confidence: 99%

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Visual Attribute Extraction Using Human Pose Estimation

Nodari

Vanetti

Gallo

2013

Pattern Recognition and Image Analysis

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Abstract. We propose a method to describe how a person is dressed, using an innovative way to extract Visual Information exploiting the Human Pose Estimation. Given the lack of algorithms in this field, we aims to pave the way giving a baseline and publishing a detailed dataset for future comparisons. In particular in this study we show how using the Human Pose Estimation, we are able to extract the essential features for the description of the Visual Attributes. Furthermore, the proposed method is able to manage the problems highlighted in literature regarding the extraction of features from images of people due to their articulated poses. For this reason we also propose a formalization of how describe people's clothing in order to give a starting point and facilitate the analysis and the Visual Attributes extraction phase. Moreover we show how the use of Deformable Structures let us to extract Visual Attributes without the using of segmentation algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual Attribute Extraction Using Human Pose Estimation

Nodari

Vanetti

Gallo

2013

Pattern Recognition and Image Analysis

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“…Kalantidis et al [3] proposed an approach, which can automatically suggest relevant clothing products. When an input image is given, they estimate the pose of the person using the same method used in [2]. After that, they segment the clothing area, which is followed by the extraction of clothing class (such as shirt, tops etc.).…”

Section: Garment Product Segmentation and Type Identificationmentioning

confidence: 99%

“…For this, they created a dataset consisting of 8000 people with annotated attributes. Yamaguchi et al [2] proposed a method for parsing clothes from fashion photographs. They introduced a dataset (Fashionista Dataset), which consisted of 158,235 photographs and then selected 685 photos that had good visibility of the whole body and covered a variety of clothing items.…”

Section: Garment Product Segmentation and Type Identificationmentioning

confidence: 99%

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An Automated System for Garment Texture Design Class Identification

2015

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Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

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AL‐DDCNN: a distributed crossing semantic gap learning for person re‐identification

Cheng

Zhan

2016

Concurrency and Computation

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SUMMARYBy the reason of the variability of light and pedestrians' appearance, it is hard for a camera to obtain a clear human figure. Person re-identification with different cameras is a difficult visual recognition task. In this paper, a novel approach called attribute learning based on distributed deep convolutional neural network model is proposed to address person re-identification task. It shows how attributes, namely the mid-level medium between classes and features, are obtained automatically and how they are employed to re-identify person with semantics when an author-topic model is used to mapping category. Besides, considering the ability to operate on raw pixel input without the need to design special features, deep convolutional neural network is employed to generate features without supervision for attributes learning model. To overcome the model's weakness in computing speed, parallelized implementations such as distributed parameter manipulation and attributes learning are employed in attribute learning based on distributed deep convolutional neural network model. Experiments show that the proposed approach achieves state-of-the-art recognition performance in the VIPeR data set and is with a good semantic explanation.

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Parsing clothing in fashion photographs

Cited by 431 publications

References 21 publications

Visual Attribute Extraction Using Human Pose Estimation

Visual Attribute Extraction Using Human Pose Estimation

An Automated System for Garment Texture Design Class Identification

AL‐DDCNN: a distributed crossing semantic gap learning for person re‐identification

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