A Comparative Performance Analysis of CBIR using Wavelet and Contourlet Transform

Parmeshwar Birajadar, Abhijit Shete
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Abstract

In the literature various algorithms for content based image retrieval (CBIR) were proposed on different variety of databases such as texture, face, and medical images. It is observed that the CBIR performance strongly depends on effective extraction of image features such as color, texture, and shape. In the proposed research work, contourlet transform based CBIR system is proposed for texture images. Similar to wavelets, texture images can be characterized via marginal distributions of subband coefficients of recently introduced contourlets. In addition, contourlets have two new properties namely directionality and anisotropy which makes them powerful in efficient representation of natural texture images. Hence, in the proposed research work, we have analyzed and compared the performance of both wavelet and contourlet schemes for content based texture image retrieval. It is observed that contourlet based method outperforms the wavelet method for highly texture images in terms of retrieval efficiency.
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基于小波变换和Contourlet变换的CBIR性能对比分析
在文献中,基于内容的图像检索(CBIR)算法被提出用于不同类型的数据库,如纹理、人脸和医学图像。观察到,CBIR的性能在很大程度上取决于图像特征(如颜色、纹理和形状)的有效提取。在本文的研究工作中,提出了基于contourlet变换的纹理图像CBIR系统。与小波相似,纹理图像可以通过最近引入的轮廓波的子带系数的边缘分布来表征。此外,轮廓波还具有方向性和各向异性两个特性,这使得轮廓波能够有效地表示自然纹理图像。因此,在提出的研究工作中,我们分析和比较了小波和contourlet两种方案在基于内容的纹理图像检索中的性能。结果表明,基于contourlet的方法在高纹理图像的检索效率上优于小波方法。
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