Abstract
Traditional saliency detection algorithms lack object semantic character, and the segmentation algorithms cannot highlight the saliency of the segmentation regions. In order to compensate for the defects of these two algorithms, the salient object segmentation model, which is a novel combination of two algorithms, is established in this paper. With the help of a priori knowledge of image boundary background traits, the K-means++ algorithm is used to cluster the pixels for each region; in line with the sensitivity of the human eye to color and with its attention mechanism, the joint probability distribution of the regional contrast ratio and spatial saliency is established. The selection of the salient area is based on the probabilities, for which the region boundary is taken as the initial curve, and the level-set algorithm is used to perform the salient object segmentation of the image. The curve convergence condition is established according to the confidence level for the segmented region, thus avoiding over-convergence of the segmentation curve. With this method, the salient region boundary is adjacent to the object contour, so the curve evolution time is shorter, and compared with the traditional Li algorithm, the proposed algorithm has higher segmentation evaluation scores, with the additional benefit of emphasizing the importance of the object.
Highlights
Salient object segmentation refers to the segmentation of important and semantic objects from an image
The traditional image segmentation algorithm is based on image features, and the image will be divided into several semantic concept areas [1,2,3], but the contribution of each region to an understanding of the image is not indicated
Salient object segmentation based on active contouring are compared with those of the method given in reference [19]
Summary
Salient object segmentation refers to the segmentation of important and semantic objects from an image. Most saliency detection algorithms are based on the ability of the human eye to distinguish the boundary of blurred objects by its contrast sensitivity; through contrast analysis to estimate the image pixels and the contribution of regions to the visual composition, an image saliency analysis model can be designed. Traditional saliency detection algorithms only highlight the importance of regions for image analysis; the regions lack object semantics. Taking the saliency regional boundary as the initial curve, the level-set algorithm is used to perform the image segmentation, and the convergence condition of the curve is established according to the confidence level for the segmented region, to avoid over-convergence of the segmentation curve In this model, each region has a clear semantic meaning, and the model highlights the importance of regional borders. In the formula, nk indicates the number of pixels in the region Rk, and N represents the number of pixels in the W × H image
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