A Binary Color Vision Framework for Content-Based Image Indexing

Abstract

We have developed an elegant and effective method for content-based color image indexing and retrieval. A color image is first represented as a sequence of binary images each captures the presence or absence of a predefined visual feature, such as color. Binary vision algorithms are then used to analyze the geometric properties of the bit planes. The size, shape, or geometry moment of each connected binary region on the visual feature planes can then be computed to characterize the image content. In this paper, we introduce the color blob size table (Cbst) as an image content descriptor. Cbst is a 2-D array that captures the co-occurrence statistics of connected regions sizes and their colors. Unlike other similar methods in the literature, Cbst enables the employment of simple numerical metric measures to compare image similarity based on the properties of region segments. We will demonstrate the effectiveness of the method through its application to content-based retrieval from image database.