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您现在的位置是:虫虫源码 > Matlab > 2014 CVPR 论文 Dirichlet-based Histogram Feature Transform for Image Classification的matlab 源代码 

2014 CVPR 论文 Dirichlet-based Histogram Feature Transform for Image Classification的matlab 源代码 

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应用背景用Dirichlet分布实现了新的 fisher vector 的实现,相比原来的fisher vector的训练的参数减少,由来有的三个变为一个,提高了训练的效率,同时也提高了训练的正确率。关键技术recent development of image classifications, such as by SIFT local descriptors. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. The (L1-normalized) histogram feature is regarded as a probability mass function, which is modeled by Dirichlet distribution. Based on the probabilistic modeling, we induce the Dirichlet Fisher kernel for transforming the histogram feature vector. The method works on the individual histogram feature to enhance the discriminative power at a low computational cost. On the other hand, in the bag-of-feature (BoF) framework, the Dirichlet mixture model can be extended to Gaussian mixture by transforming histogram-based local descriptors, e.g.,

文 件 列 表

DirFT
coding
classify
evaluation
README
script
localdesc
util
feat
myvlfeat

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