Once the image regions are identified, each region is characterized and represented using a feature set. The goal of color representation is to accurately capture the salient color characteristics of the region in a low-dimensional vector. Two choices for simple color features are (1) mean color and (2) dominant color of the region. They both use only a 3-D vector to represent the color of the region. The comparison measurement is fairly easy to compute but the discrimination is inadequate. An alternative is to use (3) color histograms. A color histogram is a high-dimensional feature vector typically having greater than 100 dimensions and the comparison of histograms is computationally intensive. They are best suited for representation of global color rather than local color regions because of storage requirements and the large number of computations required at query time.
We propose a new approach for representation of color content which is well matched to the binary color set back-projection region extraction. We represent region color using a (4) binary color set that selects only those colors which are sufficiently present in the region. Since the color sets are binary vectors, they can be indexed efficiently, i.e., using a binary tree, rather than requiring substantial computations to compare histograms at query time. The binary set representation of color regions and indexing is described in detail in the following sections.