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MVC
Classification
License: Unknown

Overview

This dataset not only has four different views for each clothing item, but also provides 264
attributes for describing clothing appearance. We adopt a state-of-the-art deep learning method
to present baseline results for the attribute prediction and clothing retrieval performance.
We also evaluate the method on a more difficult setting, cross-view exact clothing item retrieval.
This dataset can be used for further studies towards view-invariant clothing retrieval.

Data Collection

We collect the MVC dataset by crawling images from several online shopping websites, such as
Amazon.com, Zap- pos.com or Shopbop.com. The challenge in constructing the dataset is to gather
complete four different views (front, back, left, and right views) for each clothing item,
as there may be only two or three views available for some clothes.

In current stage, the
MVC dataset consists of 37,499 items and 161,638 clothing images, where most items have at
least four views. Most of the image resolutions are 1920 × 2240 pixels, which thus offers sufficient
details for various clothing related studies such as clothing attribute localization and type
classification.

Data Annotation

To measure the clothing retrieval accuracy, we propose to use clothing attributes to establish
the relevance between two images. We collect the ground truth attributes from the websites
and manually select 264 attributes for similarity evaluation.

These 264 attributes are organized
into a three-layer hier- archy. The first layer enforces the gender of clothes, which contains
two attributes, Men and Women. There are eight categories for Men’s clothes and nine categories
for Women’s clothes, where most of them overlap.

img

The third layer contains more detailed attributes. Eg., in
the branch “Women->Shirts & Tops”, there are type, color, pat- tern and style attributes. Type
includes Blouses, Button Up Shirts, T Shirts and Tank Tops. Color involves blue, brown, green,
... etc. Horizontal stripes, floral print are some at- tributes belonged to Pattern. Style
contains attributes like short-sleeve, round-neckline, long-dress.

Citation

@inproceedings{liu2016mvc,
  title={Mvc: A dataset for view-invariant clothing retrieval and attribute prediction},
  author={Liu, Kuan-Hsien and Chen, Ting-Yen and Chen, Chu-Song},
  booktitle={Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval},
  pages={313--316},
  year={2016}
}
Data Summary
Type
Image,
Amount
161.26K
Size
--
Provided by
Academia Sinica
Academia Sinica, the most preeminent academic institution of the Republic of China (Taiwan), was founded in China in 1928 to promote and undertake scholarly research in the sciences and humanities. After the ROC government moved to Taiwan in 1949, Academia Sinica was re-established in Taipei. Academia Sinica’s growth during this transition period was initially slow due to political instability and meager budgets.
| Amount 161.26K | Size --
MVC
Classification
License: Unknown

Overview

This dataset not only has four different views for each clothing item, but also provides 264
attributes for describing clothing appearance. We adopt a state-of-the-art deep learning method
to present baseline results for the attribute prediction and clothing retrieval performance.
We also evaluate the method on a more difficult setting, cross-view exact clothing item retrieval.
This dataset can be used for further studies towards view-invariant clothing retrieval.

Data Collection

We collect the MVC dataset by crawling images from several online shopping websites, such as
Amazon.com, Zap- pos.com or Shopbop.com. The challenge in constructing the dataset is to gather
complete four different views (front, back, left, and right views) for each clothing item,
as there may be only two or three views available for some clothes.

In current stage, the
MVC dataset consists of 37,499 items and 161,638 clothing images, where most items have at
least four views. Most of the image resolutions are 1920 × 2240 pixels, which thus offers sufficient
details for various clothing related studies such as clothing attribute localization and type
classification.

Data Annotation

To measure the clothing retrieval accuracy, we propose to use clothing attributes to establish
the relevance between two images. We collect the ground truth attributes from the websites
and manually select 264 attributes for similarity evaluation.

These 264 attributes are organized
into a three-layer hier- archy. The first layer enforces the gender of clothes, which contains
two attributes, Men and Women. There are eight categories for Men’s clothes and nine categories
for Women’s clothes, where most of them overlap.

img

The third layer contains more detailed attributes. Eg., in
the branch “Women->Shirts & Tops”, there are type, color, pat- tern and style attributes. Type
includes Blouses, Button Up Shirts, T Shirts and Tank Tops. Color involves blue, brown, green,
... etc. Horizontal stripes, floral print are some at- tributes belonged to Pattern. Style
contains attributes like short-sleeve, round-neckline, long-dress.

Citation

@inproceedings{liu2016mvc,
  title={Mvc: A dataset for view-invariant clothing retrieval and attribute prediction},
  author={Liu, Kuan-Hsien and Chen, Ting-Yen and Chen, Chu-Song},
  booktitle={Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval},
  pages={313--316},
  year={2016}
}
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