# Basic Algorithm for Image Classification

I have been trying to implement image classification by extracting facial features. I tried starting with some already established methods like Multi Kernel Approach, using Independent Component Analysis etc. Is there a very basic algorithm for image classification with which I can proceed the implementation ?

The data set I am currently considering is CMU Face Images data set. So aim is to separate images with human faces to those who do not contain human faces. I think I need to learn parameters by supplying positive data set and negative data set as well. I am aware that SIFT, SURF, VJ algorithm etc. are helpful fir extracting features from an image. But I am stuck on how to start i.e. what features are required for face detection, and how to proceed towards implementing them.

• Would be better if you explained what type of classification you needed. Eg. If all you wanted was bright and dark images then its very different than if you wanted to classify as with faces and without. – av501 Mar 30 '13 at 18:00
• You really need to be more specific about what you are trying to achieve. A basic algorithm is: bool detect_face(img image) { return true; } --- it's easy to implement, but not very useful. – Peter K. Mar 31 '13 at 21:10
• @PeterK. I have updated my question with more details. – krammer Apr 1 '13 at 4:50
• Perhaps you should try learning those features. SVM seems to be a popular way of doing it nowdays. Beware of the time consumption for that though. Do some basic pre-processing and use a background subtraction library to reduce the number of features beforehand. Note: SIFT/SURF/ORB all will give you a large set of features which you should use in order to get your basic set. After that pre-processing in order to reduce number of features is generally necessary. – Naresh Apr 1 '13 at 11:27
• OK, so you are looking for face detection algorithms. I've added that tag to the question. Will dig a bit now for a proper response. – Peter K. Apr 1 '13 at 15:24