Application of an Incremental SVM Algorithm for On-line Human Recognition from Video Surveillance Using Texture and Color Features

Figures 9, 10, 11 present the DET curves of the different biometric systems when using the different texture features after applying the BioHashing algorithm (i.e. BioCodes) for

In order to be fair, in the infinite feature case, the number of sampled feature con- sidered for the constraints violation checking problem (Line 5) is equal to the number of

Finally, we show the efficiency of the proposed multi-class SVM- based quality method in predicting the performance of SIFT matching system using the Equal Error Rate (EER)

Specifically, this thesis extends the classic Support Vector Machine (SVM) algorithm to present a new hill- climbing method Relevant Subset Selection Using The

The underlying idea is that it learns a metric M or a linear transformation L which brings instances close to their same class center point while it moves away from it different

In combination with the parallel architecture, however, a significant improvement of the results regarding the basic model is achieved, as well, but it gets clear that in all cases

Прямое вычисление плотности тока по формуле (20) требует решения уравнения Пуассона только в начальный момент времени и в дальнейшем все сеточные величины зависят

A detailled analysis of the algorithms indicate that LIBSVM runs best when the cache contains all the dot products involving a potential support vector and an arbitrary example: