Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a cohesion measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new CM, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM-based method has achieved promising performance for these two tasks.

中文翻译：

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通过内聚力测量发现对象


颜色和强度是图像中的两个重要组成部分。通常，颜色或强度相似的图像像素组是对象的信息表示。因此，它们特别适合计算机视觉任务，例如显着性检测和对象建议生成。然而，共享相似真实世界颜色的图像像素可能会有很大不同，因为颜色通常会因强度而失真。在本文中，我们重新研究了最初用于基于谱聚类的图像分割方法中的亲和力矩阵。为对象发现制定了一种新的亲和力矩阵，该矩阵对颜色失真具有鲁棒性。此外，还基于制定的亲和力矩阵导出对象区域的内聚力测量（CM）。基于新的CM，提出了一种新颖的对象发现方法，利用亲和力矩阵的特征向量来发现图像中潜在的对象。然后我们将所提出的方法应用于显着性检测和对象提议生成。几个评估基准的实验结果表明，所提出的基于 CM 的方法在这两项任务中取得了可喜的性能。