Matching images is a fundamental problem in image processing. The most common technique used to compare binary images is to calculate the correlation between two images or simply to subtract them. Both of these methods –as well as other matching methods– require some type of similarity operation to be applied to the whole image, and hence they are image size dependent. This implies that as image size increases, more processing time is required. However, with image sizes already exceeding 20 mega-pixels and standard image sizes doubling approximately every five years, the need to find a size invariant image matching method is becoming crucial. In this paper, we present a quick way to compare and match binary images based on the Probabilistic Matching Model (PMM). We present two simple image size invariant methods based on PMM: one for fast detection of dissimilar binary images and another for matching binary images. For detecting dissimilar binary images we introduce the Dissimilar Detection via Mapping method (DDM). We compare DDM to other popular matching methods used in the image processing arena and show that DDM is magnitudes faster than any other method. For binary image matching, we use DDM as a preprocessor for other popular methods to speed up their matching speed. In particular, we use DDM with cross correlation to speed it up. Test results are presented for real images varying in size from 16 kilo-pixel images to 10 mega-pixel images to show the method’s size invariance.