Abstract
Fashion accessory plays an important role in costume designing. A well-designed accessory consisting of different types of materials help enhance the aesthetic of the dresses. A key problem of accessory design is to find the replaceable material with appropriate aesthetic and cheaper price. However, such a process is performed manually in accessory factory, in which the work efficiency is very low. Therefore, material image retrieval is an important technique to automatic and facilitates the process of accessory design and management. In this paper, a voting-based preprocessing method is proposed to locate the material in the image. And thus a regression model is built to make use of the neighboring edge directions to optimize the robust edge direction of a point. Finally, both color and edge features will be coded as histogram-based features for representing the materials for image retrieval. Experiments have been conducted on real captured material image to validate the effectiveness of the proposed locating and searching technique.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of China under Grant 61703283, 61773328, 61672358, 61703169, 61573248, in part by the research grant of the Hong Kong Polytechnic University (Project Code: G-UA2B) in part by the China Postdoctoral Science Foundation under Project 2016M590812, Project 2017T100645 and Project 2017M612736, in part by the Guangdong Natural Science Foundation under Project 2017A030310067, Project with the title Rough Sets-Based Knowledge Discovery for Hybrid Labeled Data and Project with the title The Study on Knowledge Discovery and Uncertain Reasoning in Multi-Valued Decisions, and in part by the Shenzhen Municipal Science and Technology Innovation Council under Grant JCYJ20160429182058044.
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Meng, Y., Mo, D., Guo, X., Cui, Y., Wen, J., Wong, W.K. (2019). Robust Feature Extraction for Material Image Retrieval in Fashion Accessory Management. In: Wong, W. (eds) Artificial Intelligence on Fashion and Textiles. AITA 2018. Advances in Intelligent Systems and Computing, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-319-99695-0_36
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DOI: https://doi.org/10.1007/978-3-319-99695-0_36
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