Abstract
Pairwise neighboring relationships estimated by Gaussian weight function have been extensively adopted in the graph-based salient region detection methods recently. However, the learning of the parameters remains a problem as nonoptimal models will affect the detection results significantly. To tackle this challenge, we first apply the adjacent information provided by all neighbors of each node to construct the undirected weight graph, based on the assumption that every node can be optimally reconstructed by a linear combination of its neighbors. Then, the saliency detection is modeled as the process of graph labelling by learning from partially selected seeds (labeled data) in the graph. The promising experimental results presented on some datasets demonstrate the effectiveness and reliability of our proposed graph-based saliency detection method through linear neighborhoods.
Highlights
The goal of saliency detection is to identify and locate the most interesting and important region that pops out from the rest in an image, which has been widely used for applications in computer vision, including object detection and recognition [1, 2], image compression [3], image segmentation [4], content based image retrieval [5], image cropping [6], and photo collage [7].Numerous researches have been conducted to design various algorithms for salient region detection
Previous works on detecting salient regions from images represented as graphs include [8,9,10,11,12,13,14,15]. These models describe the input image as an undirected weight graph, in which vertices represent the image elements and edges represent the pairwise dissimilarity between vertices, and the salient object detection problem is formulated as random walks [8,9,10], binary segmentation [11, 12], labelling task [13, 14], or distance metric [15] on the graph, which aims at finding the pop-out vertices at some local or global locations
The nodes are visually homogeneous superpixels, which are computationally efficient and perceptually meaningful compared to regular image patches, and generated by the Simple Linear Iterative Clustering (SLIC) algorithm proposed by Achanta et al [19]
Summary
The goal of saliency detection is to identify and locate the most interesting and important region that pops out from the rest in an image, which has been widely used for applications in computer vision, including object detection and recognition [1, 2], image compression [3], image segmentation [4], content based image retrieval [5], image cropping [6], and photo collage [7].Numerous researches have been conducted to design various algorithms for salient region detection. Previous works on detecting salient regions from images represented as graphs include [8,9,10,11,12,13,14,15] These models describe the input image as an undirected weight graph, in which vertices represent the image elements (pixels/regions) and edges represent the pairwise dissimilarity between vertices, and the salient object detection problem is formulated as random walks [8,9,10], binary segmentation [11, 12], labelling (ranking) task [13, 14], or distance metric [15] on the graph, which aims at finding the pop-out vertices at some local or global locations. It is difficult to determine the number and location of salient seeds that the semisupervised method requires, which is a known problem
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