  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
