Image retrieval based on wavelet sub-bands and fuzzy weighted regions

Avoiding the “curse of dimensionality” in contentbased image retrieval becomes one of the most essential tasks to achieve because of the high number of stocked images as well as the high dimensionality of the descriptive vectors' space. In this context, our work consists on minimizing low-level features describing an image by using a reduced descriptor that combines color and texture information which is wavelet transformation. In fact, we propose to describe the image by high frequency subbands of discrete wavelet transformation (DWT) related to weighted salient regions after a fuzzy segmentation step. Moreover, images comparison guided by the most weighted regions is presented. Experiments and comparative study with other similar works prove the efficiency of the proposed approach for image retrieval in heterogeneous image bases.