TianZhan¹ and Austin Amakye Ansah², ¹USA, ²The University of Texas at Arlington, USA

Capturing aesthetically pleasing photographs can be challenging for amateur photographers due to the complexity of factors such as lighting, composition, and contrast. To address this issue, we propose a mobile application powered by deep learning models and regression analysis. This application analyzes real-time image frames using a pre-trained MobileNet backbone and a custom classification layer [8]. By leveraging the Aesthetics and Attributes database, the app calculates an aesthetic score for each photograph, providing instant feedback to users. Challenges encountered during development, including interfacing with machine learning models and implementing camera functionalities, are addressed. Through experiments, we evaluate different training approaches and compare our methodology with existing research. Our solution aims to empower users to capture high-quality photographs by assisting them in understanding and applying fundamental principles of photography.

Machine Learning, Mobile, Tensorflow, Flutter