The Artificial Intelligence's Fashion Dictionary in Textile Industry
Recently Google Trend displayed 20 hues of greenery ranging from Poison green, parrot green, fern, chartreuse, moss, oasis, citrine, weeping willow, palm, nile, brass to dark khaki. The International Color Authority says: “Color comes before style and price, and is the first factor to which the customer responds” (Scully, 2012). Often perceived as a never-ending and repeating cycle, styled colors and cuts significantly bring back effective extracts of people’s popularity to its fore. As such, any prediction of successful fashion trend holds great business value. And yet, fashion holds no seat for prediction. In this scenario, the combination of colors, the texture and the quality of colors with the help of technology-especially AI, ML and Big Data is continuously inspiring the Fashion Analysts, Colour cycle Analysts and Textile giants alike. Increasingly, textile and fashion industry retails and other related stockholders are inching towards relying on reviewing datasets from metadata rather than market reports. This initiative required AI to step in with ML.
The parameters of estimation, recognition, analyzing and predicting data sets saw Data Normalizing humming its way inside the echelons of fashion and textile worlds. The textile industry saw the emergence of varied fabrics with varied hues and took help of algorithms that carefully and strategically separated look-alikes fabrics and pattern dictionaries. With such a huge database from people’s, designers’, and textile bigwigs, the associated industry of textile and fashion has set its eyes on the visual representation that’s drawing its assistance from the Artificial Intelligence tools and algorithms. It’s indeed a step to relive colors, fabrics, and texture with a vigor that is all set to stay in the timeline of both-AI and fashion & textile industry.