This article delves into the development of a Fashion Recommendation System using computer vision and deep learning. This system analyzes images of individual clothing items and suggests complete, aesthetically pleasing outfits. It leverages a combination of convolutional neural network (CNN) autoencoders, multi-input CNNs, and K-Means clustering to achieve this.
The foundation of this project is the Polyvore dataset, a rich source of outfit images and associated metadata, including user likes and clothing categories. This data enables the system to learn patterns and preferences in fashion, ultimately predicting the popularity of potential outfits. The system first filters and prepares the dataset, focusing on specific outfit types like dresses with outerwear and shoes, or combinations of tops, bottoms, and optional layers like pullovers or outerwear, always including shoes and a bag.
The core of the system lies in its ability to process and understand images. Using OpenCV-Python, images are converted into numerical representations suitable for machine learning models. This involves resizing, normalization, and encoding image data into a format that can be interpreted by the algorithms. Dimensionality reduction is achieved through CNN autoencoders, trained specifically for each clothing category (e.g., dresses, tops, shoes). This process not only reduces computational complexity but also allows the system to learn generalized representations of clothing items.
A multi-input CNN is then trained to predict the likely popularity of an outfit based on the encoded representations of its constituent items. This model considers each item individually and collectively, capturing the overall aesthetic of the outfit. The training process involves feeding the model with various outfit combinations and their corresponding user likes, enabling it to learn the underlying relationships between clothing items and perceived style.
To ensure diversity in recommendations, K-Means clustering is applied to group similar outfits based on their embeddings. The top-scoring outfit from each cluster is then presented to the user, providing a range of stylish options rather than variations of a single outfit. This clustering step enhances the user experience by offering a broader spectrum of fashion choices. Finally, a user-friendly interface built with a Dataiku application allows users to input their wardrobe images and receive personalized outfit recommendations.
This fashion recommendation system represents a practical application of computer vision and deep learning in the fashion industry, offering potential for personalized styling advice, virtual wardrobe management, and enhanced retail experiences. Further development could include recommending new clothing items to complement existing wardrobes or assisting with visual merchandising in retail settings.
