AI-Powered Visual Search and Virtual Try-On for E-Commerce

The e-commerce fashion industry faces significant challenges in enhancing product discovery and improving the online shopping experience. Customers often struggle to find visually similar clothing items and accurately visualize how garments will fit and look when worn. These limitations lead to higher return rates and reduced customer satisfaction. To address these issues, this research proposes an intelligent e-commerce framework integrating Visual Search and Virtual Try-On functionalities. The Visual Search module enables users to upload an image, which is processed using a ResNet-based feature extractor. A K-Nearest Neighbors (KNN) algorithm then retrieves the top five visually similar products from a precomputed database, streamlining product discovery. The Virtual Try-On module utilizes pose detection and offset max pooling to accurately align and overlay clothing items on the user’s video input, offering a realistic preview of the garment on the user. This system enhances the online shopping experience by mitigating common challenges such as difficulty in finding similar products and uncertainty in garment fit and appearance. Future enhancements may include expanding the range of product categories, improving pose detection and clothing alignment, incorporating size recommendation models, and integrating augmented reality (AR) for an immersive shopping experience. By addressing these critical pain points, this research aims to revolutionize online fashion retail, improve customer engagement, and reduce return rates.