text: We study the problem of Salient Object Subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). To this end, we present a salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained Convolutional Neural Network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.
  @cite_1: We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that directly outputs a compact set of detection windows, if any, for an input image. Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects. Location proposals tend to be highly overlapping and noisy. Based on the Maximum a Posteriori principle, we propose a novel subset optimization framework to generate a compact set of detection windows out of noisy proposals. In experiments, we show that our subset optimization formulation greatly enhances the performance of our system, and our system attains 16-34 relative improvement in Average Precision compared with the state-of-the-art on three challenging salient object datasets.
summary: Salient object detection. Salient object detection aims at detecting dominant objects in a scene. Given a test image, some methods generate a saliency map that highlights the overall region of salient objects; other methods produce bounding boxes for localization. Ideally, if a salient object detection method can well localize each salient object, then the number of objects can be simply inferred by counting the detection windows. However, many existing salient object detection methods assume the existence of salient objects, and they are mainly tested and optimized for images that contain a single dominant object . Therefore, salient object detection methods often generate undesirable results on background images, and are prone to fail on images with multiple objects and complex background. Recently, @cite_1 proposed a salient object detection method for unconstrained images. Although this method can handle complex images to some extent, we will show that the counting-by-detection approach is less effective than our subitizing method in predicting the number of salient objects.