Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland.
IEEE Trans Pattern Anal Mach Intell. 2012 Nov;34(11):2189-202. doi: 10.1109/TPAMI.2012.28.
We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. These include an innovative cue to measure the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-art saliency measure, and the combined objectness measure to perform better than any cue alone. We also compare to interest point operators, a HOG detector, and three recent works aiming at automatic object segmentation. Finally, we present two applications of objectness. In the first, we sample a small numberof windows according to their objectness probability and give an algorithm to employ them as location priors for modern class-specific object detectors. As we show experimentally, this greatly reduces the number of windows evaluated by the expensive class-specific model. In the second application, we use objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives. As shown in several recent papers, objectness can act as a valuable focus of attention mechanism in many other applications operating on image windows, including weakly supervised learning of object categories, unsupervised pixelwise segmentation, and object tracking in video. Computing objectness is very efficient and takes only about 4 sec. per image.
我们提出了一种通用的目标物衡量标准，用于量化图像窗口中包含任何类别的目标物的可能性。我们明确地对其进行训练，以区分具有明确空间边界的目标物（如牛和电话）和无定形背景元素（如草和道路）。该度量标准结合了几个图像线索，用于衡量物体的特征，例如与周围环境不同以及具有封闭边界。其中包括一种创新的线索来衡量封闭边界特征。在具有挑战性的 PASCAL VOC 07 数据集上的实验中，我们证明了这种新线索优于最先进的显著度测量方法，而组合的目标衡量标准优于任何单独的线索。我们还将其与兴趣点算子、HOG 检测器以及三个最近旨在实现自动目标分割的工作进行了比较。最后，我们介绍了目标衡量标准的两个应用。在第一个应用中，我们根据目标物的概率对少量窗口进行采样，并提供一种算法，将它们用作现代特定类目标检测器的位置先验。正如我们在实验中所展示的，这大大减少了昂贵的特定类模型评估的窗口数量。在第二个应用中，我们将目标衡量标准作为特定类模型的补充得分，从而减少了误报。正如最近的几篇论文所表明的，目标衡量标准可以作为许多其他基于图像窗口的应用程序（包括弱监督学习的目标类别、无监督像素级分割以及视频中的目标跟踪）的有价值的注意力机制。计算目标衡量标准非常高效，每个图像只需约 4 秒。
