Probabilistic Color Modelling of Clothing Items - Al-Rawi, Mohammed; Beel, Joeran (My University, 2020)
Color modelling and extraction is an important topic in fashion. It can help build a wide range of applications,for example, recommender systems, color-based retrieval, fashion design, etc. We aim to develop and test models that can extract the dominant colors of clothing and accessory items. The approach we propose has three stages: (1) Mask-RCNN to segment the clothing items, (2) cluster the colors into a predefined number of groups, and (3) combine the detected colors based on the hue scores and the probability of each score. We use Clothing Co-Parsing and ModaNet datasets for evaluation. We also scrape fashion images from the WWW and use our models to discover the fashion color trend. Subjectively, we were able to extract colors even when clothing items have multiple colors. Moreover, we are able to extract colors along with the probability of them appearing in clothes. The method can provide the color baseline drive for more advanced fashion systems. It can also find applications in other areas, for example, interior design.
ACM's Workshop on Recommender Systems in Fashion
Keywords: Color clustering; Deep learning; Clothing; Fashion trends; k-means; Gaussian mixture model; COLOR; COLOUR; COLOUR CLUSTERING; Fashion, Clothing; Fashion/Textiles Design; Gaussian Mixture Models; Development