Salient Object Subitizing

People can immediately and precisely identify 1, 2, 3 or 4 items by a simple glance. The phenomenon, known as Subitizing, inspires us to pursue the task of Salient Object Subitizing (SOS), i.e. predicting the existence and the number of salient objects in a scene using holistic cues. To study this problem, we propose a new image dataset annotated by Amazon Mechanical Turk. We show that for a substantial proportion of our dataset, there is a high labeling consistency among different subjects, even when a very limited viewing time (0.5s) is given. On our dataset, the baseline method using the global Convolutional Neural Network (CNN) feature achieves 94% recall rate in detecting the existence of salient objects, and 42-82% recall rate (chance is 20%) in predicting the number of salient objects (1, 2, 3, and 4+), without resorting to any object localization process. Finally, we demonstrate the usefulness of the proposed subitizing technique in two computer vision applications: salient object detection and object proposal.