Graph Cut Based Local Binary Patterns for Content Based Image Retrieval

Dilkeshwar Pandey; Rajive Kumar

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Graph Cut Based Local Binary Patterns for Content Based Image Retrieval

机译：基于图割的局部二进制模式用于基于内容的图像检索

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In this paper, a new algorithm which is based on the graph cut theory and local binary patterns (LBP) for content based image retrieval (CBIR) is proposed. In graph cut theory, each node is compared with the all other nodes for edge map generation. The same concept is utilized at LBP calculation which is generating nine LBP patterns from a given 3—3 pattern. Finally, nine LBP histograms are calculated which are used as a feature vector for image retrieval. Two experiments have been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiments are Brodatz database (DB1), and MIT VisTex database (DB2). The results after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.

机译：本文提出了一种基于图割理论和局部二进制模式（LBP）的基于内容的图像检索（CBIR）算法。在图割理论中，将每个节点与所有其他节点进行比较，以生成边缘图。在LBP计算中使用了相同的概念，该计算从给定的3-3模式生成9个LBP模式。最后，计算出九个LBP直方图，这些直方图用作图像检索的特征向量。为了证明我们算法的价值，已经进行了两个实验。还要提到的是，用于实验的数据库是Brodatz数据库（DB1）和MIT VisTex数据库（DB2）。经过调查的结果表明，与LBP和其他现有的变换域技术相比，它们在评估指标上有显着改善。

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《Information and Knowledge Management》 |2011年第1期|共13页
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Dilkeshwar Pandey; Rajive Kumar;
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Graph Cut Based Local Binary Patterns for Content Based Image Retrieval

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