Feb 9, 2015
上一篇提到了SPM。这篇博客打算把ScSPM和LLC一起总结了。ScSPM和LLC其实都是对SPM的改进。这些技术,都是对特征的描述。它们既没有创造出新的特征(都是提取SIFT,HOG, RGB-histogram et al),也没有用新的分类器(也都用SVM用于最后的image classification),重点都在于如何由SIFT、HOG形成图像的特征(见图1)。从BOW,到BOW+SPM
seen from United States
seen from China
seen from United States
seen from United States
seen from United States
seen from China
seen from Philippines
seen from United States
seen from United States
seen from Nepal
seen from Ukraine
seen from United States

seen from Canada
seen from United States

seen from Malaysia
seen from Philippines
seen from Brazil
seen from United States
seen from Poland
seen from United States
上一篇提到了SPM。这篇博客打算把ScSPM和LLC一起总结了。ScSPM和LLC其实都是对SPM的改进。这些技术,都是对特征的描述。它们既没有创造出新的特征(都是提取SIFT,HOG, RGB-histogram et al),也没有用新的分类器(也都用SVM用于最后的image classification),重点都在于如何由SIFT、HOG形成图像的特征(见图1)。从BOW,到BOW+SPM