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Quadtree classified vector quantization based image retrieval scheme

机译:基于四叉树分类矢量量化的图像检索方案

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With the fast development of multimedia, it is crucial to find the way to search image database effectively. The vector quantization (VQ) based image retrieval method is popular in recent years. In this paper, we propose the quadtree classified vector quantization (QCVQ) scheme to improve the VQ method by exploiting the visual importance of image blocks and using the edge information to describe the content of each block efficiently. Moreover, we also apply the adaptive block size. The simulation results show that, compared with the previous image retrieval algorithms using VQ and chromaticity moments (CM), our proposed scheme has obviously better average retrieval rate and higher average precision.
机译:随着多媒体的快速发展,找到有效搜索图像数据库的方法至关重要。基于矢量量化(VQ)的图像检索方法是近年来流行的方法。在本文中,我们提出了一种四叉树分类矢量量化(QCVQ)方案,以通过利用图像块的视觉重要性并利用边缘信息有效地描述每个块的内容来改进VQ方法。此外,我们还应用了自适应块大小。仿真结果表明,与以前的基于VQ和色度矩(CM)的图像检索算法相比,该方案具有更好的平均检索率和更高的平均精度。

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