Chen Xianyi, Peng Xiafu, Wang Sun'an
Department of Automation, Xiamen University, Xiamen 361005, China.
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2021 Sep 24;21(19):6374. doi: 10.3390/s21196374.
Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.
超像素分割是目标识别与检测方法中的关键图像预处理步骤之一。然而，图像中平滑连接的同质区域的过分割是关键问题。这会产生冗余的复杂锯齿纹理。本文将使用密度峰值聚类来减少冗余超像素，并突出显著目标的主要纹理和轮廓。首先，提取网格像素作为特征点，并定义每个特征点的密度。其次，利用密度峰值提取聚类中心。最后，所有特征点将按密度峰值进行聚类。通过上述步骤获得的像素块即为超像素。该方法在BSDS500数据集上进行，实验结果表明，边界召回率（BR）和分割精度达成率（ASA）分别为95.0%和96.3%。此外，该方法在效率方面表现更好（30帧/秒）。对比实验表明，超像素边界不仅对显著目标的主要纹理和轮廓具有良好的贴合度，而且还能有效减少同质区域中的冗余超像素。
