Clothing Category Classification using Common Models Adaptively Adjusted to Observation

Jingyu Hu, Nobuyuki Kita, Yasuyo Kita

2020

Abstract

This paper proposes a method of automatically classifying the category of clothing items by adaptively adjusting common models subject to each observation. In the previous work(Hu and Kita, 2015), we proposed a two-stage method of categorizing a clothing item using a dual-arm robot. First, to alleviate the effect of large physical deformation, the method reshaped a clothing item of interest into one of a small number of limited shapes by using a fixed basic sequence of re-grasp actions. The shape was then matched with shape potential images of clothing category, each of which was configured by combining the clothing contours of various designed items of the same category. However, there was a problem that the shape potential images were too general to be highly discriminative. In this paper, we propose to configure high discriminative shape potential images by adjusting them subject to observation. Concretely, we restrict the contours used for potential images according to simply observable information. Two series of experiments using various clothing items of five categories demonstrate the effect of the proposed method.

Paper Citation

in Harvard Style

Hu J., Kita N. and Kita Y. (2020). Clothing Category Classification using Common Models Adaptively Adjusted to Observation. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 310-317. DOI: 10.5220/0008936903100317

in Bibtex Style

@conference{visapp20,
author={Jingyu Hu and Nobuyuki Kita and Yasuyo Kita},
title={Clothing Category Classification using Common Models Adaptively Adjusted to Observation},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={310-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008936903100317},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Clothing Category Classification using Common Models Adaptively Adjusted to Observation
SN - 978-989-758-402-2
AU - Hu J.
AU - Kita N.
AU - Kita Y.
PY - 2020
SP - 310
EP - 317
DO - 10.5220/0008936903100317
PB - SciTePress