A Content-Based Automatic Filter Recommendationsystem for Photography and Image Editing using Artificial Intelligence
Year: 2024
Type: conference-paper
Abstract: This paper presents the development and evaluation of PhotoGleam, a mobile application designed to enhance image quality through AI-generated filters.The app addresses the common issue of aesthetically unappealing colors in mobile phone photography by leveraging advanced AI models to create custom filters tailored to each image. The paper details the implementation of the AI-driven image processing engine, the backend Flask server, and the Flutter frontend user interface. Through two key experiments, the effectiveness of AI-generated filters was assessed in terms of both image quality and user engagement. Results showed that users consistently preferred AI-generated filters over standard ones, and the introduction of these filters led to increased time spent on the app and more images edited. While there are areas for improvement, such as server scalability and AI model reliance, PhotoGleam demonstrates significant potential as a valuable tool for enhancing mobile photography.
Authors: Yifan Luo, Yu Sun
Institution: California State Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404448563