Object-driven image group annotation
In this paper, we propose a three-stage method for annotating image groups in real-world photo image databases: (1) Image databases are automatically divided into several image groups so that most photos in each group were taken in the same scene using the time information associated with each photo; (2) Objects in each image are recognized using multiclass object recognition; (3) Each image group is categorized into a scene using all object labels from (2) in the image group. Our main contribution is to propose a novel method for annotating image groups using all objects recognized from all images in this image group. We train our method on 6,000 objects in 696 images from the LabelMe dataset and verify the effectiveness of our proposed method on real-world photo databases consists of 4 outdoor scenes.