Kernel region approximation blocks for indexing heterogonous databases

This paper presents a new indexing method for visual features in high dimensional vector space using region approximation approach. The proposed method is designed to combine the values of the heterogeneous features in the same index structure; it determines nonlinear relationship between features so that more accurate similarity comparison between vectors can be supported. The basic idea is to map the data vectors into a feature space via a nonlinear kernel; the feature space is partitioned into regions. An efficient approach to approximate regions is proposed with the corresponding upper and lower distance bounds. To evaluate our technique, we conducted several experiments for searching the nearest K neighbours. The obtained results show the interest of our method.