Abstract: A system and method indexes an image database by partitioning an image thereof into a plurality of cells, combining the cells into intervals and then spots according to perceptual criteria, and generating a set of spot descriptors that characterize the perceptual features of the spots, such as their shape, color and relative position within the image. The shape preferably is a derivative of the coefficients of a Discrete Fourier Transform (DFT) of the perimeter trace of the spot. The set of spot descriptors forms as an index entry for the spot. This process repeated for the various images of the database. To search the index, a key comprising a set of spot descriptors for a query image is generated and compared according to a perceptual similarity metric to the entries of the index. The metric determines the perceptual similarity that the features of the query image match those of the indexed image. The search results are presented as a scored list of the indexed images.

Abstract: 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).