I have a task of classifying videos based on feature vectors generated for each frame in the videos. The individual features are numbers between 0 and 5, and the feature vectors have about 20 elements. There aren't that many videos to train on (and they vary in length) so I don't think just concatenating the framewise features would work. Currently (following the example of a paper on the subject) I'm generating a vector for each video that measures whether each feature exceeds a threshold in each frame of the video, but I expect there are other possible approaches (such as ones that don't throw away all the chronological information from the framewise vectors). What other kinds of things could I do? If it's relevant, my classifier is an SVM.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.