I trained an Adaboost classifier to distinguish between two artistic styles. A tecnichal report of my results can be found on my ResearchGate.net account. This sort of tutorial – or more precisely collection of blog posts – explains the steps and provides the code to create an image classifier from histograms of oriented edges, colors and intensities. Therefore you can replicate my methodology to any other problems.
There are two main steps on this: (1) produce the features of the images, and (2) train and use the classifier. I started the blog sequence from the classifier that I used (Adaboost), and then continue explaining how to produce features for big collections. Probably this is a weird way of viewing the problem because I am starting from the last step,however I found that most of the decisions I took in the process were justified by the input I wanted to reach. I also recommend to check the comments where I have answered multiple questions during the time of existance of this posts.
- Adaboost on OpenCV 2.3
- Color and intensities histograms in one vector (Octave/Matlab)
- Producing local and global color/intensity histograms (0ctave/Matlab)
- Lots of features from color histograms on a directory of images (0ctave/Matlab)
- Edge orientation histograms in global and local features (0ctave/Matlab)
- Lots of features from edge orientation histogram on a directory of images (0ctave/Matlab)