This paper presents a co-salient object detection method to find common salient regions in a set of images. We utilize deep saliency networks to transfer co-saliency prior knowledge and better capture high-level semantic information. The resulting initial co-saliency maps are enhanced by seed propagation steps over an integrated graph. The deep saliency networks are trained in a supervised manner to avoid weakly supervised online learning and exploit them not only to extract high-level features but also to produce both intra- and inter-image saliency maps. Through a refinement step, the initial co-saliency maps can uniformly highlight co-salient regions and locate accurate object boundaries. To handle input image groups inconsistent in size, we propose to pool multi-regional descriptors including both within-segment and within-group information. In addition, the integrated multilayer graph is constructed to find the regions that the previous steps may not detect by seed propagation with low-level descriptors. In this paper, we utilize the useful complementary components of high- and low-level information and several learning-based steps. Our experiments have demonstrated that the proposed approach outperforms comparable co-saliency detection methods on widely used public databases and can also be directly applied to co-segmentation tasks.
In this paper, we introduce a method to detect co-saliency from an image pair that may have some objects in common. The co-saliency is modeled as a linear combination of the single-image saliency map (SISM) and the multi-image saliency map (MISM). The first term is designed to describe the local attention, which is computed by using three saliency detection techniques available in literature. To compute the MISM, a co-multilayer graph is constructed by dividing the image pair into a spatial pyramid representation. Each node in the graph is described by two types of visual descriptors, which are extracted from a representation of some aspects of local appearance, e.g., color and texture properties. In order to evaluate the similarity between two nodes, we employ a normalized single-pair SimRank algorithm to compute the similarity score. Experimental evaluation on a number of image pairs demonstrates the good performance of the proposed method on the co-saliency detection task. This paper presents a new algorithm to solve the problem of co-saliency detection, i.e., to find the common salient objects that are present in both of a pair of input images. Unlike most previous approaches, which require correspondence matching, we seek to solve the problem of co-saliency detection under a preattentive scheme. Our algorithm does not need to perform the correspondence matching between the two input images, and is able to achieve co-saliency detection before the focused attention occurs. The joint information provided by the image pair enables our algorithm to inhibit the responses of other salient objects that appear in just one of the images. Through experiments we show that our algorithm is effective in localizing the co-salient objects inside input image pairs. Image triage is a common task in digital photography. Determining which photos are worth processing for sharing with friends and family and which should be deleted to make room for new ones can be a challenge, especially on a device with a small screen like a mobile phone or camera. In this work we explore the importance of local structure changes?e.g. human pose, appearance changes, object orientation, etc.?to the photographic triage task. We perform a user study in which subjects are asked to mark regions of image pairs most useful in making triage decisions. From this data, we train a model for image saliency in the context of other images that we call cosaliency. This allows us to create collection-aware crops that can augment the information provided by existing thumbnailing techniques for the image triage task. Image co-saliency detection is a valuable technique to highlight perceptually salient regions in image pairs. In this paper, we propose a self-contained co-saliency detection algorithm based on superpixel affinity matrix. We first compute both intra and inter similarities of superpixels of image pairs. Bipartite graph matching is applied to determine most reliable inter similarities. To update the similarity score between every two superpixels, we next employ a GPU-based all-pair SimRank algorithm to do propagation on the affinity matrix. Based on the inter superpixel affinities we derive a co-saliency measure that evaluates the foreground cohesiveness and locality compactness of superpixels within one image. The effectiveness of our method is demonstrated in experimental evaluation.