Abstract One of the fundamental properties of a salient object region is its contrast with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by using object proposal algorithms. These return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated. In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD).
Among the first object proposal methods the work of @cite_11 , named the Constrained Parametric Min-Cuts (CPMC) method, uses graph cuts with different random seeds to obtain multiple binary foreground and background segments. @cite_0 proposes to measure the objectness of an image window, where they rank randomly sampled image windows based on their likelihood of containing the object by using multiple cues among which edges density, multiscale saliency, superpixels straddling and color contrast. @cite_23 proposed an object proposal method similar to the CPMC method by generating multiple foreground and background segmentations. A very fast method for object proposals was proposed by @cite_4 , which generates box proposals at 300 images per second.
@cite_0 @cite_4 @cite_23 @cite_11
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Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of considering the contrast between the salient objects and their surrounding regions, we consider both foreground and background cues in a different way. We rank the similarity of the image elements (pixels or regions) with foreground cues or background cues via graph-based manifold ranking. The saliency of the image elements is defined based on their relevances to the given seeds or queries. We represent the image as a close-loop graph with super pixels as nodes. These nodes are ranked based on the similarity to background and foreground queries, based on affinity matrices. Saliency detection is carried out in a two-stage scheme to extract background regions and foreground salient objects efficiently. Experimental results on two large benchmark databases demonstrate the proposed method performs well when against the state-of-the-art methods in terms of accuracy and speed. We also create a more difficult benchmark database containing 5,172 images to test the proposed saliency model and make this database publicly available with this paper for further studies in the saliency field. This paper proposes an effective salient object segmentation method via the graph-based integration of saliency and objectness. Based on the superpixel segmentation result of the input image, a graph is built to represent superpixels using regular vertex, background seed vertex with the addition of a terminal vertex. The edge weights on the graph are defined by integrating the difference of appearance, saliency, and objectness between superpixels. Then, the object probability of each superpixel is measured by finding the shortest path from the corresponding vertex to the terminal vertex on the graph, and the resultant object probability map can generally better highlight salient objects and suppress background regions compared to both saliency map and objectness map. Finally, the object probability map is used to initialize salient object and background, and effectively incorporated into the framework of graph cut to obtain the final salient object segmentation result. Extensive experimental results on three public benchmark datasets show that the proposed method consistently improves the salient object segmentation performance and outperforms the state-of-the-art salient object segmentation methods. Furthermore, experimental results also demonstrate that the proposed graph-based integration method is more effective than other fusion schemes and robust to saliency maps generated using various saliency models. Interactive segmentation is useful for selecting objects of interest in images and continues to be a topic of much study. Methods that grow regions from foreground background seeds, such as the recent geodesic segmentation approach, avoid the boundary-length bias of graph-cut methods but have their own bias towards minimizing paths to the seeds, resulting in increased sensitivity to seed placement. The lack of edge modeling in geodesic or similar approaches limits their ability to precisely localize object boundaries, something at which graph-cut methods generally excel. This paper presents a method for combining geodesic-distance information with edge information in a graphcut optimization framework, leveraging the complementary strengths of each. Rather than a fixed combination we use the distinctiveness of the foreground background color models to predict the effectiveness of the geodesic distance term and adjust the weighting accordingly. We also introduce a spatially varying weighting that decreases the potential for shortcutting in object interiors while transferring greater control to the edge term for better localization near object boundaries. Results show our method is less prone to shortcutting than typical graph cut methods while being less sensitive to seed placement and better at edge localization than geodesic methods. This leads to increased segmentation accuracy and reduced effort on the part of the user. We present a novel technique for figure-ground segmentation, where the goal is to separate all foreground objects in a test image from the background. We decompose the test image and all images in a supervised training set into overlapping windows likely to cover foreground objects. The key idea is to transfer segmentation masks from training windows that are visually similar to windows in the test image. These transferred masks are then used to derive the unary potentials of a binary, pairwise energy function defined over the pixels of the test image, which is minimized with standard graph-cuts. This results in a fully automatic segmentation scheme, as opposed to interactive techniques based on similar energy functions. Using windows as support regions for transfer efficiently exploits the training data, as the test image does not need to be globally similar to a training image for the method to work. This enables to compose novel scenes using local parts of training images. Our approach obtains very competitive results on three datasets (PASCAL VOC 2010 segmentation challenge, Weizmann horses, Graz-02).