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
翻译:图像提取和描述是计算机视觉的一个重要主题,因为它是图像重建、缝合、登记和识别等许多任务的起点。本文提出了两个新的图像特征:信息比率和相互信息比率。IR是单一图像的一个特征,而MIR描述的是两个或两个以上图像的共同特征。我们首先介绍IR和MIR,并在信息理论背景下激励这些特征,将其作为强度水平的自我信息相对于同一强度的像素所含信息的比重。值得注意的是,IR和MIR与图像输入率和相互信息的关系,讨论了典型的信息措施。最后,这些特征的效力通过在INRIA复制日数据集上的特征提取和在牛津省Affine Covariant地区的特征匹配来测试。这些数字评价证实了IR和MIR在计算机实际视觉任务中的关联性。这些数字评价证实了IR和MIR在计算机实际视觉任务中的关联性。