Color Indexing with Weak Spatial Constraints

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 of retrieval 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.