Salient Object Detection Via Two-Stage Graphs

Despite recent advances made in salient object detection using graph theory, the approach still suffers from accuracy problems when the image is characterized by a complex structure, either in the foreground or background, causing erroneous saliency segmentation. This fundamental challenge is mainly attributed to the fact that most of existing graph-based methods take only the adjacently spatial consistency among graph nodes into consideration. In this paper, we tackle this issue from a coarse-to-fine perspective and propose a two-stage-graphs approach for salient object detection, in which two graphs having the same nodes but different edges are employed. Specifically, a weighted joint robust sparse representation model, rather than the commonly used manifold ranking model, helps to compute the saliency value of each node in the first-stage graph, thereby providing a saliency map at the coarse level. In the second-stage graph, along with the adjacently spatial consistency, a new regionally spatial consistency among graph nodes is considered in order to refine the coarse saliency map, assuring uniform saliency assignment even in complex scenes. Particularly, the second stage is generic enough to be integrated in existing salient object detectors, enabling to improve their performance. Experimental results on benchmark datasets validate the effectiveness and superiority of the proposed scheme over related state-of-the-art methods

Salient object subitizing

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 14 K 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.This research was supported in part by US NSF Grants 0910908 and 1029430, and gifts from Adobe and NVIDIA. (0910908 - US NSF; 1029430 - US NSF)https://arxiv.org/abs/1607.07525https://arxiv.org/pdf/1607.07525.pdfAccepted manuscrip

Unconstrained salient object detection via proposal subset optimization

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.http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Unconstrained_Salient_Object_CVPR_2016_paper.htmlPublished versio