Wavelet-based region indexing for advanced content-based image retrieval systems

Image and video indexing have become important with recent increase in digital image collections. A majority of proposed indexing techniques in the literature are based on features extracted from the entire image. In this paper, images are segmented by the K-mean clustering algorithm in order to allow searching, and retrieving at region level. Farther, the regions are classified into object and non-object classes as we may retrieve regions based on features tailored for the corresponding type. The object class contains clumped regions while the non-object class contains regions that scattered in the entire image scenes as constrained by (Chi) 2 statistic. The performance of the retrieval of four region indexing techniques, Histogram and three Wavelet-based techniques, are evaluated. The four indexing techniques are suitable for general domain image collections. The evaluation result has shown that, although, the three wavelet-based region indexing techniques provide a good comparable performances for non-object region queries, the histogram-based region indexing technique outperforms the three wavelet-based region indexing techniques for object region queries. The histogram technique is more suitable for indexing object regions. In the other hand the wavelet-based techniques are more suitable for non-object region indexing.