Spatial Color Indexing Using Rotation, Translation, and Scale Invariant Anglograms

Abstract

As color plays an essential role in image composition, many color indexing techniques have been studied for content-based image retrieval. This paper examines the use of a computational geometry-based spatial color indexing methodology for effective and efficient image retrieval. In this scheme, an image is evenly divided into a number of M * N non-overlapping blocks, and each individual block is abstracted as a unique feature point labeled with its spatial location and dominant colors. For each set of feature points labeled with the identical color, we construct a Delaunay triangulation and then compute the feature point histogram by discretizing and counting the angles produced by this triangulation. The concatenation of all these feature point histograms serves as the image index, the so-called color anglogram . An important contribution of this work is to encode the spatial color information using geometric triangulation, which is rotation, translation, and scale invariant. We have compared the proposed approach with two of the best performing of recent spatial color indexing schemes, Color-WISE and the color correlogram approaches, respectively, at image block and pixel levels of different granularity. Various experimental results demonstrate the efficacy of our techniques.