基于Bags of Features的图像识别算法，非常经典的一片文章，论文+源码

MATLAB2014a 编译打包时出现Test checkout of feature 'Compiler' failed 安装文件中的步骤操作即可

机器学习的利器！ 特征选择的法宝！ kaggle 必备书！ -----Shi Long

Python Tricks A Buffet of Awesome Python Features高清pdf版本，英文原版

j2me在eclipse下的插件，eclipseme.feature_1.7.9_site

解决matlab 2014b的Test checkout of feature 'Compiler' failed. 以上错误据说是因为2014b破解不完全引起的。

博客 http://www.cnblogs.com/wangguchangqing/p/4333873.html 中的demo代码，鉴于有很多人想参考下，就上传到这了。 主要是匹配后，计算两视图的基础矩阵F，然后再除去不适合的匹配点. 代码中 好像还有一部分三角计算的代码

n many data analysis tasks, one is often confronted with very high dimensional data. Feature selection techniques are designed to find the relevant feature subset of the original features which can facilitate clustering, classification and retrieval. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. Traditional feature selection methods address this issue by selecting the top ranked features based on certain scores computed independently for each feature. These approaches neglect the possible correlation between different features and thus can not produce an optimal feature subset. Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, we propose here a new approach, called {\em Multi-Cluster/Class Feature Selection} (MCFS), for feature selection. Specifically, we select those features such that the multi-cluster/class structure of the data can be best preserved. The corresponding optimization problem can be efficiently solved since it only involves a sparse eigen-problem and a L1-regularized least squares problem. It is important to note that MCFS can be applied in superised, unsupervised and semi-supervised cases. If you find these algoirthms useful, we appreciate it very much if you can cite our following works: Papers Deng Cai, Chiyuan Zhang, Xiaofei He, "Unsupervised Feature Selection for Multi-cluster Data", 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10), July 2010. Bibtex source Xiaofei He, Deng Cai, and Partha Niyogi, "Laplacian Score for Feature Selection", Advances in Neural Information Processing Systems 18 (NIPS'05), Vancouver, Canada, 2005 Bibtex source

该源代码是针对《FSIM: A Feature Similarity Index for Image Quality Assessment》使用文中的PC和GM计算方式。FSIM是一种常用的进行图像质量评估的工具

Integral_Channel_Features以及代码还有一小段视频