A Measure of Perceptual Aliasing in Image Descriptors

Researchers exploring problems in image matching tasks face the curse of perceptual aliasing that is originally used in characterizing a sensing process. Perceptual aliasing occurs when the one-to-one mapping relations between world states (objects) and their representations (descriptors) are not maintained. In this paper, we introduce a novel method for quantifying perceptual aliasing. Our method measures the discriminating power of an image descriptor in terms of its ability to distinguish between images of different objects and to match images of the same object. To illustrate our method, we apply it in the evaluation of popular global image descriptors that do not require local feature or keypoint detection. Specifically, our method runs spectral clustering on the similarity matrix computed with descriptors of known image clusters and measures the performance of an image descriptor by its ability to maintain the original clusters, using two indices, MRI-1 and MRI-2, that are based on Rand index.