Abstract
Recently, salient object detection based on the graph model has attracted extensive research interest in computer vision because the graph model can represent the relationship between two regions better. However, it is difficult to capture the high-level relationship between multiple regions. In this algorithm, the input image is segmented into superpixels first. Then, a weighted hypergraph model is established using fuzzy C-means clustering algorithm and a new weighting strategy. Finally, the random walk algorithm is used to sort all superpixels on the weighted hypergraph model to obtain the salient object. The experimental results on three benchmark datasets demonstrate that the proposed method performs better than some other state-of-the-art methods.
Highlights
In computer vision, salient object detection is one of the most fundamental problems, which automatically identifies important and informative regions of an image or video based on human visual mechanisms
In order to overcome the shortcomings of the simple graph model, the hypergraph model was introduced into the field of salient object detection
In order to improve the adaptivity of hypergraph models, Han et al [11] propose a salient object detection algorithm based on adaptive multiscale hypergraph models, which can build corresponding hypergraph models adaptively according to the range of R, G, and B channels of the image pixel values
Summary
Salient object detection is one of the most fundamental problems, which automatically identifies important and informative regions of an image or video based on human visual mechanisms. According to the existing problems of the salient object detection models, in Mathematical Problems in Engineering this paper, a novel salient object detection algorithm is proposed based on a weighted hypergraph model and random walk. Ji et al [24] propose a bottom-up salient object detection method that uses the geodesic distance between image features to construct the affinity matrix and a Laplacian matrix and uses the manifold ranking and multilayer cellular automata to form the saliency maps. Aimed at the above problems, this paper proposes a salient object detection algorithm based on the weighted hypergraph model and random walk. A weighted hypergraph model is built by using the FCM algorithm at the superpixel level to obtain more complete structural information in the image for the integrity of salient object detection.
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