论文标题
计算图像特征及其匹配的信息和共同信息比率
The Information & Mutual Information Ratio for Counting Image Features and Their Matches
论文作者
论文摘要
特征提取和描述是计算机视觉的重要主题，因为它是许多任务的起点，例如图像重建，缝线，注册和识别等。在本文中，提出了两个新的图像特征：信息比（IR）和相互信息比（MIR）。 IR是单个图像的一个特征，而MIR描述了在两个或多个图像中常见的特征。我们首先引入IR和MIR并在信息理论上下文中激励这些特征，因为强度水平的自我信息比相同强度的像素的信息与所包含的信息相比。值得注意的是，讨论了IR和MIR与图像熵和相互信息的关系，即经典信息度量。最后，这些特征的有效性是通过在Inria Copledays数据集中提取的特征提取，并在牛津仿射协变区域匹配的特征。这些数值评估验证了IR和MIR在实际计算机视觉任务中的相关性
Feature extraction and description is an important topic of computer vision, as it is the starting point of a number of tasks such as image reconstruction, stitching, registration, and recognition among many others. In this paper, two new image features are proposed: the Information Ratio (IR) and the Mutual Information Ratio (MIR). The IR is a feature of a single image, while the MIR describes features common across two or more images.We begin by introducing the IR and the MIR and motivate these features in an information theoretical context as the ratio of the self-information of an intensity level over the information contained over the pixels of the same intensity. Notably, the relationship of the IR and MIR with the image entropy and mutual information, classic information measures, are discussed. Finally, the effectiveness of these features is tested through feature extraction over INRIA Copydays datasets and feature matching over the Oxfords Affine Covariant Regions. These numerical evaluations validate the relevance of the IR and MIR in practical computer vision tasks
