| Abstract: | Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Although classical wavelet transform is effective in representing image feature and is suitable in CBIR, it still encounters problems especially in implementation; e.g. floatingpoint operation and decomposition speed. We use the advantages of lifting scheme, a novel spatial approach for constructing biorthogonal wavelet filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low computational complexity as well as flexible adaptivity, revealing its potentials in CBIR. In this paper, we use Haar and Daubechies (D4) lifting schemes to decompose color images into multilevel scale and wavelet coefficients, with which we perform image feature extraction and similarity match by means of F-norm theory. Furthermore, we also provide a progressive image retrieval strategy to achieve flexible CBIR. The retrieval performances are compared with those of its classical counterpart in terms of retrieval accuracy and speed, the results outperforms its classical counterpart. We also compared our algorithm with existing Wavelet histogram technique.
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