We propose a content based image retrieval system based on object extraction through image segmentation. A general and powerful multiscale segmentation algorithm automates the segmentation process, the output of which is assigned novel colour and texture descriptors which are both effi¬cient and effective. Query strategies consisting of a semi-automated and a fully automated mode are developed which are shown to produce good results. We then show the supe¬riority of our approach over the global histogram approach which proves that the ability to access images at the level of objects is essential for CBIR.

The rapid development of modern data collection and data warehouse technologies, data mining is becoming more and more a standard practice. Data distortion is a critical component to preserve privacy in security-related data mining applications. We propose a wavelet-based method for data distortion, and compare it with the Singular Value Decomposition (SVD) based method. The experimental results show that the wavelet based method can obtain similar performance as SVD based method in preserving privacy as well as maintaining utility of the datasets, however, the computational time used by the wavelet based method is much less than the SVD based method. We conclude that the Wavelet based method is a very promising data distortion method.