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首页> 外文期刊>Malaysian Journal of Computer Science >Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
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Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)

机译：使用完整的本地三元模式（CLTP）进行医疗，场景和事件图像类别识别

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Many of texture descriptors are proposed based on the Local Binary Pattern (LBP) and have been achieved remarkable texture classification accuracy such as Completed LBP (CLBP) and Completed Local Binary Count (CLBC). However, the LBP suffers from two weaknesses where: 1) it is sensitive to noise and; 2) it sometimes classify two or more different patterns falsely to the same class. To overcome the LBP weaknesses, we propose a new texture descriptor which is defined as Completed Local Ternary Pattern (CLTP). The CLTP was used for rotation invariant texture classification. It demonstrates superior texture classification accuracy as compared to CLBP and CLBC descriptors. This is because, the CLTP is more robust to noise and has a high discriminating property that achieves impressive classification accuracy rates. In this paper, two types of experiments are carried out. In the first experiment, different amount of additive Gaussian noise is added to the TC10 Outex texture data set to investigate and prove the robustness of the CLTP against the noise. For the second experiment, the performance of CLTP for image category recognition is studied and investigated. A variety of image datasets are used in the experiments such as scene data set (e.g., Oliva and Torralba datasets (OT8)), Event sport datasets, 2D HeLa medical images, and our new scene data set, defined as USM scene data set. The experimental results proved the superiority of the CLTP descriptor over the original LBP, and different new texture descriptors such as CLBP in the image category recognition, as well as the robustness against the noise. In 2D HeLa medical images, the proposed CLTP has achieved the highest state of the art classification rate reaching 95.62%.
机译：许多纹理描述符是基于局部二进制模式（LBP）提出的，并且已经获得了非凡的纹理分类精度，例如Completed LBP（CLBP）和Completed Local Binary Count（CLBC）。但是，LBP有两个缺点：1）它对噪声敏感;以及2）有时会错误地将两个或多个不同的模式分类为同一类。为了克服LBP的弱点，我们提出了一个新的纹理描述符，该描述符被定义为Completed Local Tentary Pattern（CLTP）。 CLTP用于旋转不变纹理分类。与CLBP和CLBC描述子相比，它展示了卓越的纹理分类准确性。这是因为CLTP对噪声更鲁棒，并且具有很高的区分性，可实现令人印象深刻的分类准确率。在本文中，进行了两种类型的实验。在第一个实验中，将不同数量的加性高斯噪声添加到TC10 Outex纹理数据集，以研究和证明CLTP对抗噪声的鲁棒性。对于第二个实验，研究并研究了CLTP在图像类别识别中的性能。实验中使用了各种图像数据集，例如场景数据集（例如Oliva和Torralba数据集（OT8）），赛事运动数据集，2D HeLa医学图像以及我们定义为USM场景数据集的新场景数据集。实验结果证明了CLTP描述符优于原始LBP，以及不同的新纹理描述符（如CLBP）在图像类别识别中的优越性，以及对噪声的鲁棒性。在二维HeLa医学图像中，提出的CLTP达到了最高的最新分类率，达到95.62％。

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