PERCEPTUAL SIMILARITY IMAGE RETRIEVAL METHOD

Method of effective search and retrieval through large collections of images is presented. Search criteria are based on perceptual similarity of images. Method is capable to process such images as bitonal, gray-scale and colorful, either of artificial or “real” world origin. Continuos-tone “real scene” images such as digitized still pictures and video frames are the primary objects of interest. Search operation starts from creating of an index out of a query image. Next, the index is to be applied as a key for searching through the index database. Index creation is a multi-step procedure, wherein the essential stages are: dissection of an image into areas of free shapes (spots) and computation of certain properties for each one. The properties are shape, color and relative position within the whole image. Spot shape is stored as derivatives of coefficients of DFT applied to a spot perimeter trace. Thus, every spot is represented by a set of numerical values (spot descriptor). Each image is represented in the database by plurality of its spot descriptors. A metric to compare spots in terms of their perceptual similarity is provided. Search results are presented as a scored list of relevant images where the score is based on their perceptual similarity to a query image.