We propose two different approaches. The first one uses a multidimensional indexing structure based on approximation approach or filtering, which is an improvement in the RA-Blocks method. It is based on the proposal of an algorithm of subdividing the data space which improves the storage capacity of the index and the CPU times. In the second approach, we propose a multidimensional indexing method suitable for heterogeneous data (color, texture, shape, etc.). It combines a non-linear dimensionality reduction technique with a multidimensional indexing approach based on approximation. This combination allows not only to deal with the curse of dimensionality scalability problems but also to exploit the properties of the non-linear space to find suitable similarity measurement related to the kind of the used data.  Finally,  relevance feedback mechanism based on statistical approach is implemented and the whole system is integrated to IMALBUM. 

This work has been achieved during the PhD thesis of Imane Daoudi