Efficient Image Retrieval with Statistical Color Descriptors

Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. A distance measure is then used to measure the (dis-)similarity between images based on the descriptions of their color distributions in order to quickly find relevant images. The development and investigation of statistical methods for robust representations of such distributions, the construction of distance measures between them and their applications in efficient retrieval, browsing, and structuring of very large image databases are the main contributions of the thesis. In particular we have addressed the following problems in CBIR. Firstly, different non-parametric density estimators are used to describe color information for CBIR applications. Kernel-based methods using nonorthogonal bases together with a Gram-Schmidt procedure and the application of the Fourier transform are introduced and compared to previously used histogram-based methods. Our experiments show that efficient use of kernel density estimators improves the retrieval performance of CBIR. The practical problem of how to choose an optimal smoothing parameter for such density estimators as well as the selection of the histogram bin-width for CBIR applications are also discussed. Distance measures between color distributions are then described in a differential geometry-based framework. This allows the incorporation of geometrical features of the underlying color space into the distance measure between the probability distributions. The general framework is illustrated with two examples: Normal distributions and linear representations of distributions. The linear representation of color distributions is then used to derive new compact descriptors for color-based image retrieval. These descriptors are based on the combination of two ideas: Incorporating information from the structure of the color space with information from images and application of projection methods in the space of color distribution and the space of differences between neighboring color distributions. In our experiments we used several image databases containing more than 1,300,000 images. The experiments show that the method developed in this thesis is very fast and that the retrieval performance achieved