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
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摘要: 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.Abstract: In the extraction process of LBP features,most consumption of time and memory were paid for clustering.In order to address these problems,an improved local binary algorithm for image categorization was proposed.The algorithm replaced decimal system encoding LBP with binary descriptor.Meanwhile,Hamming distance 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 PASCAL VOC 2007 dataset showed that the adopted local binary descriptor was better than the classical LBP,specifically for time consumption.
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