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Multi-scale image segmentation method with visual saliency constraints and its application

机译：具有视觉显着性约束的多尺度图像分割方法及其应用

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Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi-scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works better for texture image segmentation than traditional multi-scale image segmentation methods, and can enable us to give priority control to the saliency objects of interest. This method has been used in image quality evaluation, scattered residential area extraction, sparse forest extraction and other applications to verify its validation. All applications showed good results.
机译：基于对象的图像分析方法比基于像素的方法具有很多优势，因此是当前的研究热点之一。为了进行基于对象的图像分析，通过多尺度图像分割获得图像对象非常重要。目前流行的图像分割方法主要采用自下而上的分割原理，实现简单，目标边界准确。然而，难以考虑图像区域的宏观统计特性，并且难以避免碎片分割（或过度分割）的结果。另外，在信息提取，目标识别和其他应用方面，图像目标并不是同等重要的，即某些特定目标或具有特定特征的目标组比其他目标更值得关注。为了避免过度分割的问题并突出感兴趣的目标，本文提出了一种具有视觉显着性图约束的多尺度图像分割方法。采用视觉显着性理论和典型特征提取方法，获得视觉显着性信息，尤其是待分析的宏观信息。视觉显着性信息用作同质权重的分布图，其中为每个像素赋予权重。该权重是多尺度图像分割中的合并约束之一。结果，宏观上属于同一对象但局部不同的像素更有可能被分配给一个相同的对象。另外，由于视觉显着性模型的约束，在基于不同对象的分割过程中，可以很好地控制局部宏观特性的约束能力。这些控件将改善分割结果中视觉显着区域的完整性，同时会稀释非显着背景区域的控制效果。实验表明，与传统的多尺度图像分割方法相比，该方法对纹理图像分割的效果更好，并且可以使我们对感兴趣的显着性对象进行优先控制。该方法已用于图像质量评估，分散居住区提取，稀疏森林提取和其他应用中，以验证其有效性。所有应用均显示出良好结果。

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