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Feature extraction is the key technology in the process of image retrieval based on content. This paper puts forward an improved algorithm of image retrieval based on combined BTC color moments and DT-CWT. We firstly select the YIQ color space as the feature extracted space due to the strong correlation between each component of RGB color space. Combined with block coding thought, this paper encodes three component images in YIQ color space into a binary bitmap and calculates the BTC color moment to represent the image color feature.In order to overcome the shortcomings of traditional wavelet transform direction, we make use of DT-CWT to extract statistical characteristics of each sub-band as texture features. Finally, we carry out the weighted summation of similarity degree of the extracted color and texture features to constitute the basis of image retrieval.The experimental results show that the color and texture features extracted by the above-mentioned algorithm have more advantages in image retrieval, which has a higher average precision than the similar algorithms.

An Improved Algorithm of Image Retrieval Based on Combined BTC Color Moments and DT-CWT