We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L1-, L2-, or L-distance between two cumulative color histograms can be used to define a similarity measure of these two color distributions. We show that while this method produces only slightly better results than color histogram methods, it is more robust with respect to the quantization parameter of the histograms. The second technique is an example of a new approach to color indexing. Instead of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which is used for the retrieval is a weighted sum of the absolute differences between corresponding moments. Our tests clearly demonstrate that a retrieval based on this technique produces better results and runs faster than the histogram-based methods.

No abstract

No abstract

To improve the discrimination power of color indexing techniques we encode a minimal amount of spatial information in the index. We propose an approach that lies between uniformly tesselating the images with rectangular regions and relying on fully segmented images. For each image we define 5 partially overlapping, fuzzy regions. From each region in the image we extract the first three moments of the color distribution and store them in the index. The feature vectors in the index are relatively insensitive to small translations and small rotations of an image because they are extracted from fuzzy regions. To retrieve images we define a function which measures the similarity of two color feature vectors. Invariance ofretrieval results with respect to the typical image rotations of 90 degrees around the center of the image is guaranteed because our feature similarity function exploits the spatial arrangement of the 5 image regions.We present experimental results using an image database which contains more than 11,000 color images. Our experiments demonstrate clearly that our weak encoding of spatial information significantly increases the discrimination power of the index compared to plain color indexing techniques.