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Originally a classifier was trained to determine if frames were adjacent but due to the highly skewed set of training examples (only two adjacent frames out of a set of n examples) the classifier had poor performance. Train an SVM classifier to determine if frames are adjacent. steps of the Code : Detect features in video frames The purpose is to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges. I have a question, do the implementation of SVM in Matlab using fitcsvm and fitcecoc already contain scaling for the dataset (ex:for image classification) or we need to do that before running the fitcecoc function? Thank you in advance. Multi-class classification for large database (matlab) matlab,machine-learning,classification,svm,libsvm. If you want to use liblinear for multi class classification, you can use one vs all technique. For more information Look at this. But if you have large database then use of SVM is not recommended. As Run time complexity of SVM is O(N * N ...