Signature-based Perceptual Nearness: Application of Near Sets to Image Retrieval

This paper presents a signature-based approach to quantifying perceptual nearness of images. A signature is defined as a set of descriptors, where each descriptor consists of a real-valued feature vector associated with a digital image region (set of pixels) combined with a region-based weight. Tolerance near sets provide a formal framework for our application of near sets to image retrieval. The tolerance nearness measure t NM was created to demonstrate application of near set theory to the problem of image correspondence. A new form of t NM has been introduced in this work, which takes into account the region size. Our method is compared to two other well-known image similarity measures: earth movers distance (EMD) and integrated region matching (IRM).