Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 7, Issue 2


Published
on

July 15, 2022


Pages

573-584


DOI

Article

Garment Image Retrieval based on Grab Cut Auto Segmentation and Dominate Color Method


Authors

Hong Liu Affiliation:
School of Fashion, Henan University of Engineering, Zhengzhou 451191, China
, Yan Wang Affiliation:
Collaborative Innovation Center of Modern Clothing Technology, Minjiang University, Fuzhou 350121, China
, Dongsheng Chen Affiliation:
Collaborative Innovation Center of Modern Clothing Technology, Minjiang University, Fuzhou 350121, China
, Jia Lv Affiliation:
Collaborative Innovation Center of Modern Clothing Technology, Minjiang University, Fuzhou 350121, China
and Riyad Alshalabi Affiliation:
College of Administrative Sciences, Applied Science University, Bahrain


Abstract

Targeted at the harmful effects of garment image retrieval at present, a new approach of garment image retrieval featured in satisfactory performance is proposed. In this study, the Grab Cut auto segmentation algorithm is applied first to segment garment images and extract the image’s foreground. And then, the color coherence vector (CCV) and the dominant color method are adopted to extract the color features to conduct garment image retrieval. The experimental data show that the Grab Cut auto segmentation algorithm is capable of extracting the foreground of garment images with either simple or complex background. Meanwhile, the data also indicate that compared with the garment image retrieval by extracting color features using CCV, extracting color features by dominating color method shows both higher accuracy and recall rates.


Keywords

Garment Image Retrieval, Grad Cut Auto Segmentation, Color Coherence Vector, Dominate Color Method, 68U10


Citation

Liu, H., Wang, Y., Chen, D., Lv, J., & Alshalabi, R. (2022). Garment image retrieval based on grab cut auto segmentation and dominate color method. Applied Mathematics and Nonlinear Sciences, 7(2), 573–584. https://doi.org/10.2478/amns.2022.2.0042

Published by: Engineering Journals

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