Wardrobe Wizard: ML-Driven Outfit Planning

Abstract

Introduction: This research paper presents the design and implementation of a Wardrobe Wizard, it is a cutting-edge system that is used to integrate the outfit planning using generative AI and machine learning for analyzing the clothing items. The system will allow the users to upload the wardrobe items or the clothing images. This clothing image or the wardrobe data is processed to extract the features, classify them into categories, and store the information in the database for smooth future reference and efficient data retrieval. By utilizing advanced ML models like CNN and Generative AI, the Wardrobe Wizard will help the user to interact with the wardrobe content and it will also answer the real time queries. The system’s architecture ensures seamless integration of clothing classification, outfit recommendation and AI interaction, which contribute to make it a user-friendly platform.

Objectives: Wardrobe Wizard provides a unique solution, compared to the ordinary wardrobe applications, which focuses on real-time capabilities and scalability. Moreover, the paper searches for potential system enhancements, such as multi-language wardrobe support, personalized user experiences through authentication, and optimization for mobile platforms. The future advancement can involve integrating virtual try-on, trend analysis and predictive models for deeper insights into fashion content. Wardrobe Wizard displays the potential of fashion technology and Generative AI, which offers an efficient way to analyze and interpret the wardrobe data.

Methods: The Wardrobe Wizard system allows the users to upload the clothing images or provide the wardrobe data for the processing. The system then extracts the features, and then classifies them into categories. It can also extract additional metadata if any of the wardrobe items have it. This information is then stored into the database for efficient retrieval and future preferences. Then the system further uses the AI generated recommender engine, so that the users can interact with the wardrobe content and get real- time answers to all of the queries.

Results: The Wardrobe Wizard using the Generative-AI System is definitely a new, innovative product changing the face of interaction with wardrobe content. Combining clothing classification, metadata extraction, and AI-driven recommendation capabilities, the system makes wardrobe content accessible and more user-friendly.

Conclusions: The project illustrates a huge leap in how people consume and interact with wardrobe content. It combines clothing recognition and generative AI to create an efficient, interactive, and user- centric solution. A system that is indeed a huge leap forward for smarter wardrobe content analysis, making it accessible and leading the way for further advancements in the field.