Recognizing and Curating Photo Albums via Event-Specific Image Importance
This work addresses the need for better personal photo management tools, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of automatic photo album organization by simultaneously recognizing event types and predicting image importance, achieving improved performance through an iterative updating procedure that allows each task to enhance the other.
Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection. In this paper, we attempt to simultaneously solve both tasks: album-wise event recognition and image- wise importance prediction. We collected an album dataset with both event type labels and image importance labels, refined from an existing CUFED dataset. We propose a hybrid system consisting of three parts: A siamese network-based event-specific image importance prediction, a Convolutional Neural Network (CNN) that recognizes the event type, and a Long Short-Term Memory (LSTM)-based sequence level event recognizer. We propose an iterative updating procedure for event type and image importance score prediction. We experimentally verified that image importance score prediction and event type recognition can each help the performance of the other.