Extension of higher order local autocorrelation features
Extension of higher order local autocorrelation features
复制标题
DOI：
10.1016/j.patcog.2006.10.006
复制
发表时间：
2005-07
期刊：
影响因子：
--
通讯作者：
Takahiro Toyoda;O. Hasegawa
中科院分区：
文献类型：
--
作者：
Takahiro Toyoda;O. Hasegawa
本研究探讨了广泛适用于图像分析的有效图像特征。我们专门解决高阶局部自相关（HLAC）功能，这是在各种应用中使用。原始HLAC特征被限制到二阶，并由25个掩模图案表示。我们增加了他们的订单8和提取扩展HLAC功能使用223掩模模式。此外，我们创建大的掩模图案和构造多分辨率功能，以支持大位移区域。在纹理分类和人脸识别中，该方法优于高斯马尔可夫随机场，Gabor特征和局部二值模式算子。
This study investigates effective image features that are widely applicable in image analysis. We specifically address higher order local autocorrelation (HLAC) features, which are used in various applications. The original HLAC features are restricted up to the second order and are represented by 25 mask patterns. We increase their orders up to eight and extract the extended HLAC features using 223 mask patterns. Furthermore, we create large mask patterns and construct multi-resolution features to support large displacement regions. In texture classification and face recognition, the proposed method outperformed Gaussian Markov random fields, Gabor features, and local binary pattern operator.