An Algorithm On Image Retrieval With Dual-Tree Complex Wavelet Transform

As the common feature of image, texture analysis is an important research method of CBIR, but most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we compared the retrieval results of DT-CWT and the traditional method for extracting texture-GLCM (Gray- Level Co-occurrence Matrix), then propose a novel approach for texture image retrieval by using dual-tree complex wavelet transform (DT-CWT). It gives texture information oriented in six different directions. Experimental results indicate that the improvement in the precision of retrieval. Six different distance metrics are tested for their performance of image retrieval. We present a new Euclidean distance for images, which we call Image Euclidean Distance (IMED).Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. It is found that Canberra and Euclidean distance measures perform well for image retrieval, and achieved the higher retrieval performance.