Spatial Encoding Using Differences of Global Features

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 reduce this shortcoming is to store feature vectors of subregions. However, this procedure increases the size of the index vector. The paper suggests to store only the differences of the features between a region and its 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 tesselation. Experimental results, using an image database with more than 12’000 color images, are presented. The combined index is as powerful as an index which is 2.5 times larger in size relying on global color features, only. Moreover, the IHD is invariant to linear color transformation ensuring a stable performance of the index under gamma corrections.