An Intelligent Photographing Guidance System Based on Compositional Deep Features and Intepretable Machine Learning Model

Photography is the activity of recording precious moments which are often difficult to make up afterwards. Therefore, taking the correct picture under proper guidance assistance is important. Although there are many factors that can determine a good photo, in general, photos that do not follow the composition rules usually look bad that make the viewer feel uncomfortable. Acting as a solution, in this paper, we propose an intelligent photographing guidance system using machine learning. The guidance is based on a tree-based interpretable machine learning model that can give reasons for decisions. There are two categories of features for guidance, which are traditional image features and deep features. Traditional features include prominent lines and image maps, such as saliency map and sharpness map, each of which exists in a multi-scale Gaussian pyramid. Deep features are extracted during the establishment of a CNN-based image composition classifier. We use these two categories of features as inputs for the interpretable machine learning model to establish a feasible photographing guidance system. The guidance system references our composition classifier with precision rate of 94.8%, and recall rate of 95.0% where the comprising tree-based interpretable model is capable of guiding camera users to alter image contents for obtaining better aesthetical compositions to take photos of good quality.