论文标题
使用块强度和梯度差（BIGD）描述符的纹理分类
Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
论文作者
论文摘要
在本文中，我们提出了一个有效且独特的局部描述符，即阻碍强度和梯度差（BIGD）。在图像贴片中，我们随机采样多尺度块对，并利用成对块的强度和梯度差异来构建本地BIGD描述符。随机抽样策略和多尺度框架有助于BigD描述符在不同方向和空间粒度水平下捕获斑块的独特模式。我们使用本地汇总描述符（VLAD）或改进的Fisher矢量（IFV）的向量将本地BIGD描述符编码为完整的图像描述符，然后将其送入线性支持向量机（SVM）分类器中以进行纹理分类。我们通过评估五个公共纹理数据集（包括Brodatz，Curet，KTH-TIPS和KTH-TIPS-2A和-2B）的分类性能，将提议的描述符与典型和最先进的描述符进行比较。实验结果表明，与最先进的纹理描述符，密集的微块差异（DMD）相比，提出的具有更强判别能力的BIGD描述符的分类精度高0.12％〜6.43％。
In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and gradient differences of pairwise blocks to construct the local BIGD descriptor. The random sampling strategy and the multi-scale framework help BIGD descriptors capture the distinctive patterns of patches at different orientations and spatial granularity levels. We use vectors of locally aggregated descriptors (VLAD) or improved Fisher vector (IFV) to encode local BIGD descriptors into a full image descriptor, which is then fed into a linear support vector machine (SVM) classifier for texture classification. We compare the proposed descriptor with typical and state-of-the-art ones by evaluating their classification performance on five public texture data sets including Brodatz, CUReT, KTH-TIPS, and KTH-TIPS-2a and -2b. Experimental results show that the proposed BIGD descriptor with stronger discriminative power yields 0.12% ~ 6.43% higher classification accuracy than the state-of-the-art texture descriptor, dense microblock difference (DMD).
