While histogram or global feature approaches are powerful methods to encode image information for retrieval purposes, they suffer from a complete lack of spatial information. One possibility to overcome this drawback is the storage of the feature vectors of subregions. However, this increases the size of the index vector. The paper suggests to store only the differences of the features between a region and its subregions, instead the whole feature vector of subregions. This introduced distance is called inter hierarchical distance (IHD). A new index, which combines the IHD and global color feature of the whole image, is suggested. The subregions are gained by a fixed tessellation. Experimental results, using an image database with more than 12'000 color images, are presented. The retrieval power of the combined index is as powerful as an index which is 2.5 times larger in size and just needs global color features. The IHD is invariant to linear color transformation, which ensures a more stable performance of the index under gamma corrections.