In this paper we propose two saliency models for salient object segmentation based on a hierarchical image segmentation, a tree-like structure that represents regions at different scales from the details to the whole image (e.g. gPb-UCM, BPT). The first model is based on a hierarchy of image partitions. The saliency at each level is computed on a region basis, taking into account the contrast between regions. The maps obtained for the different partitions are then integrated into a final saliency map. The second model directly works on the structure created by the segmentation algorithm, computing saliency at each node and integrating these cues in a straightforward manner into a single saliency map. We show that the proposed models produce high quality saliency maps. Objective evaluation demonstrates that the two methods achieve state-of-the-art performance in several benchmark datasets. HighlightsModel for salient object segmentation based on hierarchies of image partitions.High quality saliency maps.Detection of salient objects of different scales with accurate boundaries.Performance comparable to state-of-the-art techniques in terms of precision-recall.
Zhu @cite_10 construct an undirected graph by connecting adjacent SLIC superpixels and formulate the salient object detection problem as the optimization of the saliency values of the superpixels. The cost function is defined in terms of background and foreground weights that measure the boundary connectivity and the contrast of superpixels.
@cite_10
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To facilitate efficiency, most recent successful saliency detection methods are built on superpixel level. However, saliency detection with single-scale superpixel segmentation may fail in capturing the intrinsic salient objects in complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem and realize more reliable saliency detection, we present a simple strategy using multiscale superpixels to jointly detect salient object via low-rank analysis. Specifically, we construct a multiscale superpixel pyramid and derive the corresponding saliency map using multiple saliency features and priors for each single scale at first. Then, we show that by joint low-rank analysis of multiscale saliency maps, we can obtain a more reliable adaptively fused saliency map that takes all scales saliency results into account. We further propose a GMM-based co-saliency prior to enable the above approach to detecting co-salient objects from multiple images. Extensive experiments on benchmark datasets validate the effectiveness and superiority of the proposed approach over state-of-the-art methods. We propose an unsupervised bottom-up saliency detection approach by exploiting novel graph structure and background priors. The input image is represented as an undirected graph with superpixels as nodes. Feature vectors are extracted from each node to cover regional color, contrast and texture information. A novel graph model is proposed to effectively capture local and global saliency cues. To obtain more accurate saliency estimations, we optimize the saliency map by using a robust background measure. Comprehensive evaluations on benchmark datasets indicate that our algorithm universally surpasses state-of-the-art unsupervised solutions and performs favorably against supervised approaches. Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hyper graph that utilizes a set of hyper edges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyper edges in the hyper graph. The main advantage of hyper graph modeling is that it takes into account each pixel's (or region's) affinity with its neighborhood as well as its separation from image background. Furthermore, we propose an alternative approach based on center-versus-surround contextual contrast analysis, which performs salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experimental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the state-of-the-art approaches to salient object detection. 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.