AI-Powered Personal Stylist and Outfit Recommendation System using Computer Vision

Outfit suggestion has emerged as one of the most significant challenges in the area of computer vision and intelligent fashion systems that have the aim to assist users to select aesthetically pleasing and image-compatible combinations of clothes. Traditional methods of recommendations such as rule-based systems, collaborative filtering, and metadata-based methods are limited to cold-start problems, subjective labeling, and the inability to understand visual style and appearance. To overcome these limitations, this study proposes a deep learning-based outfit recommendation system that relies on the computer vision approach to evaluate photos of clothing and recommend the right outfits. The proposed system applies convolutional neural networks (CNNs) which are utilized to extract visual features in the form of color, texture, pattern, and style of clothes based on clothing photos. Then, the outfit suitability is based on similarity learning, and the algorithm manages to extract the explicit and subtle fashion features and provide accurate and scalable outfit recommendations after combining similarity- based recommended learning with deep visual feature learning. The approach suits the modern fashion and internet stores because it is introduced as an interactive application with the ability to provide real-time suggestions on outfits.