怎么让作研究简化呢?
偷懒!
怎么偷懒呢?
用库!
所以推荐个库
有这些东西:
• The VLFeat library
• Caltech-101running example
• Visual descriptors
- Vector Quantization (Elkan, vl_kmeans, vl_kdtreebuild,
vl_kdtreequery)
- Spatial histograms (vl_binsum, vl_binsearch)
• Learning and classification
- Fast linear SVMs
- PEGASOS (vl_pegasos)
- Fast non-linear SVMs
- Homogeneous kernel maps (vl_homkermap)
• Other VLFeat features
• The VLFeat library- SIFT example (vl_sift)• Caltech-101 running example• Visual descriptors- PHOW feature (fast dense SIFT, vl_phow)- Vector Quantization (Elkan, vl_kmeans, vl_kdtreebuild,vl_kdtreequery)- Spatial histograms (vl_binsum, vl_binsearch)• Learning and classification- Fast linear SVMs- PEGASOS (vl_pegasos)- Fast non-linear SVMs- Homogeneous kernel maps (vl_homkermap)• Other VLFeat features
做识别阿,分类阿的同志要看下
还是GPL
还支持MAC\WINDOWS\LINUX
加州理工真不简单,101就这样了…..
Caltech-101是一个dataset
…….
感谢匿名同学的修正:
纠正一下,这个库是UCLA毕业的 Andrea Vedaldi 开发的,现在在Oxford 。