Using Basic Image Features for Texture Classification
Using Basic Image Features for Texture Classification
复制标题
DOI：
10.1007/s11263-009-0315-0
复制
发表时间：
2010-07-01
影响因子：
19.5
通讯作者：
Griffin, L. D.
中科院分区：
文献类型：
--
作者：
Crosier, M.;Griffin, L. D.
将纹理图像统计地表示为局部特征离散词汇表上的直方图已被证明对纹理分类任务非常有效。图像由局部向量描述，例如，对某些滤波器组的响应；视觉词汇表被定义为这个描述符-响应空间的一个分区，通常基于聚类。在本文中，我们研究了一种基于Griffin和Lillholm的基本图像特征（Proc. SPIE 6492(09):1- 11,2007）而不是聚类的方法的性能，该方法将纹理表示为几何定义的视觉词汇表上的直方图。bif提供了一个自然的数学量化的滤波器响应空间到定性不同类型的局部图像结构。我们还扩展了处理班级内部规模变化的方法。我们的算法很简单：不需要像基于聚类的方法那样进行预训练步骤来学习视觉字典，也不需要调整参数来处理不同的数据集。我们在三个流行且具有挑战性的纹理数据集上测试了我们的实现，并发现它在每个数据集上都产生了一致的良好分类结果，包括我们认为KTH-TIPS和UIUCTex数据库报告的最佳结果。
Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases.