Content-Based Superpixel Segmentation and Matching Using Its Region Feature Descriptors
Content-Based Superpixel Segmentation and Matching Using Its Region Feature Descriptors
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DOI：
10.1587/transinf.2019edp7322
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发表时间：
2020-08
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影响因子：
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通讯作者：
Jianmei Zhang;Pengyu Wang;F. Gong;Hongqing Zhu;Ning Chen
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文献类型：
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作者：
Jianmei Zhang;Pengyu Wang;F. Gong;Hongqing Zhu;Ning Chen
寻找同一物体或场景的两幅图像之间的对应关系是计算机视觉中的一个活跃研究领域。提出了一种快速有效的基于内容的超像素图像匹配与拼接（CSIS）方法。与流行的基于关键点的匹配方法不同，我们的方法提出了一种基于超像素内部特征的方案来实现图像匹配。首先，我们使用了一种新型的基于内容特征表示的超像素生成算法，称为基于内容的超像素分割（CSS）算法。超像素是使用颜色、空间和梯度特征信息在新的距离度量方面生成的。它的发展，以平衡紧凑性和边界粘附的超像素。然后，我们计算每个超像素的熵，以分离出一些具有显著特征的超像素。接下来，对于每个所选超像素，通过提取和融合所选超像素本身的局部特征来生成其多特征描述符。最后，我们比较候选超像素及其邻域的匹配特征，以估计两幅图像之间的对应关系。我们使用我们的超像素区域描述符对复杂和可变形表面上的超像素匹配和图像拼接进行了评估，结果表明，新方法在匹配精度和执行速度方面是有效的。
SUMMARY Finding the correspondence between two images of the same object or scene is an active research field in computer vision. This paper develops a rapid and effective Content-based Superpixel Image matching and Stitching (CSIS) scheme, which utilizes the content of superpixel through multi-features fusion technique. Unlike popular keypoint-based matching method, our approach proposes a superpixel internal feature-based scheme to implement image matching. In the beginning, we make use of a novel superpixel generation algorithm based on content-based feature representation, named Content-based Superpixel Segmentation (CSS) algorithm. Superpixels are generated in terms of a new distance metric using color, spatial, and gradient feature information. It is developed to balance the compactness and the boundary adherence of resulted super-pixels. Then, we calculate the entropy of each superpixel for separating some superpixels with significant characteristics. Next, for each selected superpixel, its multi-features descriptor is generated by extracting and fusing local features of the selected superpixel itself. Finally, we compare the matching features of candidate superpixels and their own neighborhoods to estimate the correspondence between two images. We evaluated superpixel matching and image stitching on complex and deformable surfaces using our superpixel region descriptors, and the results show that new method is effective in matching accuracy and execution speed.