一种用于图像分类的局部二值改进算法

An improved local binary algorithm for image categorization

针对LBP特征提取过程中聚类耗时、占用内存较大的问题,提出了一种用于图像分类的局部二值改进算法.该算法用二进制描述符替代LBP中的十进制表示、用汉明距离替代欧氏距离进行特征聚类,同时将不同尺度的LBP特征进行融合,实现了多尺度图像局部二值描述.将提出的改进算法,尤其是多尺度融合特征,在PASCAL VOC 2007数据库与经典LBP方法进行对比分析,实验结果表明,本算法正确率更高,运行效率也有很大提高.

In the extraction process of LBP features, most consumption of time and memory were paid for cluste-ring. In order to address these problems,an improved local binary algorithm fposed. The algorithm replaced decimal system encoding LBP with binary descriptor. Meanwhile ,Hamming dis-tance was emploied rather than Euclidean metric for features clustering. The multi-scale LBP features was flued for a new local binary descriptor. The result of the experiment on the Pthe adopted local binary descriptor was better than the classical LBP, specifically for time consumption.