Improved opponent color local binary patterns: an effective local image descriptor for color texture classification
Improved opponent color local binary patterns: an effective local image descriptor for color texture classification
复制标题
DOI：
10.1117/1.jei.27.1.011002
复制
发表时间：
2018-01-01
影响因子：
1.1
通讯作者：
Napoletano, Paolo
中科院分区：
文献类型：
--
作者：
Bianconi, Francesco;Bello-Cerezo, Raquel;Napoletano, Paolo
纹理分类在许多计算机视觉应用中起着重要作用。本地二进制模式（LBP）编码方案已在很大程度上被证明是非常有效的这项任务。改进的LBP（ILBP）概念简单，易于实现，并且基于点到平均阈值方案而不是点对点方案的高效LBP变体。我们建议使用这种编码方案提取通道内和通道间的颜色纹理分类功能。我们通过实验评估了所得到的改进的对手颜色LBP单独和级联与ILBP的局部颜色对比度图上的一组图像分类任务超过9个数据集的通用颜色纹理和11个数据集的生物医学纹理。所提出的方法在几乎所有考虑的数据集中都优于其他灰度和彩色LBP变体，并且即使与上一代卷积神经网络的图像特征相比也具有竞争力，特别是对于生物医学图像的分类。(c)2017 SPIE和IS&T
Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intraand interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images. (c) 2017 SPIE and IS&T