A fast method for training support vector machines with a very large set of linear features
Proceedings. IEEE International Conference on Multimedia and Expo - Tập 1 - Trang 309-312 vol.1
Tóm tắt
Current systems for object detection often use support vector machines (SVM) as the basic classification algorithm. A rather common case is to compute a small set of linear features and then train the classifier on these features. We present a fast method to train and evaluate SVM with many linear features and show results for face detection using a set of 210400 features. The resulting classifier is both more accurate and faster than a classifier trained on raw pixel features, which total up only to 576 features in our case.
Từ khóa
#Support vector machines #Face detection #Object detection #Principal component analysis #Support vector machine classification #Classification algorithms #Kernel #Runtime #Fourier transforms #Wavelet domainTài liệu tham khảo
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