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Multi-Scale Colour Completed Local Binary Patterns for Scene and Event Sport Image Categorisation

机译:用于场景和事件运动图像分类的多尺度彩色完成局部二值模式

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摘要

The Local Binary Pattern (LBP) texture descriptor and some of its variant descriptors have been successfully used for texture classification and for a few other tasks such as face recognition, facial expression, and texture segmentation. However, these descriptors have been barely used for image categorisation because their calculations are based on the gray image and they are only invariant to monotonic light variations on the gray level. These descriptors ignore colour information despite their key role in distinguishing the objects and the natural scenes. In this paper, we enhance the Completed Local Binary Pattern (CLBP), an LBP variant with an impressive performance on texture classification. We propose five multiscale colour CLBP (CCLBP) descriptors by incorporating five different colour information into the original CLBP. By using the Oliva and Torralba (OT8) and Event sport datasets, our results attest to the superiority of the proposed CCLBP descriptors over the original CLBP in terms of image categorisation.
机译:本地二进制模式(LBP)纹理描述符及其某些变体描述符已成功用于纹理分类和其他一些任务,例如面部识别,面部表情和纹理分割。但是,这些描述符几乎没有用于图像分类,因为它们的计算基于灰度图像,并且仅对灰度上的单调光变化不变。尽管这些描述符在区分对象和自然场景方面起着关键作用,但它们忽略了颜色信息。在本文中,我们增强了“完整局部二进制模式”(CLBP),它是一种LBP变体,在纹理分类方面具有令人印象深刻的性能。通过将五个不同的颜色信息合并到原始CLBP中,我们提出了五个多尺度颜色CLBP(CCLBP)描述符。通过使用Oliva和Torralba(OT8)和赛事运动数据集,我们的结果证明了在图像分类方面,建议的CCLBP描述子优于原始CLBP。

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