A universal photography suggestion system utilizing composition detection, orientation detection, and subject position detection

Abstract

Photography is one of the most popular hobby and images are one of the most important content types on social media, and the impact of a photo often hinges on its composition as much as its subject. In response to this, we proposed a system that classifies the compositional structure, detects orientation and subject of a given photo and suggests improvements based on established photography rules. For the classification of the composition, the photo will be categorized into one of five classes(CC, ROT, LL, FIF, PAT). Then, it will determine the orientation of an image. Lastly, this system uses YOLOv8 object detection model to find the objects of a photograph and through logics and conditions the subject is determined. The proposed system will provide the final suggestion based on the three results of the three proposed models. The main goal of the research is to develop a suggestion system that utilizes the detection models built using Deep Learning(DL) algorithms and find the optimal models that will accurately determine the composition, orientation and subject (if any) of a photograph. We have achieved up to 74.34% accuracy in our composition detection model and a minimum of 0.5870 mean square error (MSE) on our orientation detection model. The subject detection conditions capable of properly detecting the subject of an image most of the cases. Our approach aims to assist users in improving their photography skills and elevating the quality of visual content on any media platforms.