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Shape-Based Image Retrieval Using Pair-Wise Candidate Co-ranking

机译:基于形状的图像检索使用配对候选共同排名

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Shape-based image retrieval is one of the most challenging aspects in Content-Based Image Retrieval (CBIR). A variety of techniques are reported in the literature that aim to retrieve objects based on their shapes; each of these techniques has its advantages and disadvantages. In this paper, we propose a novel scheme that exploits complementary benefits of several shape-based image retrieval techniques and integrates their assessments based on a pair-wise co-ranking process. The proposed scheme can handle any number of CBIR techniques; however, three common techniques are used in this study: Invariant Zernike Moments (IZM), Multi-Triangular Area Representation (MTAR), and Fourier Descriptor (FD). The performance of the proposed scheme is compared with that of each of the selected techniques. As will be demonstrated in this paper, the proposed co-ranking scheme exhibits superior performance.
机译:基于形状的图像检索是基于内容的图像检索(CBIR)中最具挑战性的方面之一。在文献中报告了各种技术,旨在根据其形状检索物体;这些技术中的每一种都具有其优点和缺点。在本文中,我们提出了一种新颖的方案,该方案利用了几种基于形状的图像检索技术的互补益处,并基于对智能协调过程集成了他们的评估。所提出的方案可以处理任何数量的CBIR技术;然而,本研究中使用了三种常见技术:不变的Zernike矩(IZM),多三角形区域表示(MTAR)和傅立叶描述符(FD)。将所提出的方案的性能与每个所选技术的性能进行比较。如本文将说明的,所提出的共控制方案表现出卓越的性能。

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