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The following paper presents a novel and effective algorithm for Content-Based Image Retrieval. To design it we used the Dual-Tree Complex Wavelet Transform for image feature extraction and Hausdorff distance to compute similarity distance between the feature vector of the query-image and all the image feature vectors stored in the image database. Then, we performed experiments to estimate the effectiveness of the proposed algorithm which showed high values of precision.

The following paper presents a comparative analysis on the efficiency of two search algorithms for Content-Based Image Retrieval. Both algorithms are designed using the Dual-Tree Complex Wavelet Transform for image feature extraction and Hausdorff distance for similarity distance computation. The difference in the steps of the algorithms produces difference in the final result. To estimate the efficiency of the algorithms, we performed some experiments and compared their results. They clearly show that one of the algorithms provides much higher retrieval rate and system performance than the other.