Spectral Segmentation via Midlevel Cues Integrating Geodesic and Intensity
Spectral Segmentation via Midlevel Cues Integrating Geodesic and Intensity
复制标题
通过集成测地线和强度的中层线索进行光谱分割
DOI：
10.1109/tcyb.2013.2243432
复制
发表时间：
2013-12-01
影响因子：
11.8
通讯作者：
Li, Xuelong
中科院分区：
文献类型：
--
作者：
Lu, Huchuan;Zhang, Ruixuan;Li, Xuelong
在涉及复杂自然场景的图像处理和模式识别中，图像分割仍然是一个挑战。在本文中，我们提出了一个新的亲和度模型的光谱分割的基础上中层线索。与大多数直接对低级别线索进行操作的现有方法相比，我们首先将图像过度分割成超像素图像，然后将测地线边缘和强度线索整合在一起以形成相似性矩阵W，从而更准确地描述数据之间的相似性。测地线边缘可以避免强边界，代表两个超像素之间的真实边界，而平均红绿色蓝色矢量可以更好地描述超像素的强度。据我们所知，这是一种全新的亲和模型来表示超像素。在此基础上，利用超像素级的谱聚类，实现像素级的图像分割。实验结果表明，该方法在各种自然图像上都能取得稳定、良好的效果。评估比较还证明，我们的方法达到了相当的准确性，并显着优于大多数国家的最先进的算法。
Image segmentation still remains as a challenge in image processing and pattern recognition when involving complex natural scenes. In this paper, we present a new affinity model for spectral segmentation based on midlevel cues. In contrast to most existing methods that operate directly on low-level cues, we first oversegment the image into superpixel images and then integrate the geodesic line edge and intensity cue to form the similarity matrix W so that it more accurately describes the similarity between data. The geodesic line edge could avoid strong boundary and represent the true boundary between two superpixels while the mean red green blue vector could describe the intensity of superpixels better. As far as we know, this is a totally new kind of affinity model to represent superpixels. Based on this model, we use the spectral clustering in the superpixel level and then achieve the image segmentation in the pixel level. The experimental results show that the proposed method performs steadily and well on various natural images. The evaluation comparisons also prove that our method achieves comparable accuracy and significantly performs better than most state-of-the-art algorithms.