PCA-based representation of color distributions for color-based image retrieval

In many color based image retrieval systems the color properties of an image are described by the histogram of the colors in the image. Color histograms are insensitive to small object distortions and easy to compute. Histogram based searches are however often inefficient for the large histogram sizes needed to represent color distributions. Therefore we introduce several new, PCA-based methods that provide efficient representations of color histograms and differences between two color histograms. We also investigate distance measures in the space of histograms which are defined by quadratic forms and which take into account the geometric structure of the underlying color space. We show that the combination of the quadratic forms based distance measure and the compression of the histogram information by difference based PCA-approximations provide new powerful and efficient retrieval algorithms for color based image retrieval.