Video event detection using generalized subclass discriminant analysis and linear support vector machines

In this paper, a two-phase approach to event detection in video is proposed. This combines a novel nonlinear Discriminant Analysis (DA) method called Generalized Subclass DA (GSDA), to identify a discriminant subspace, and a Linear Support Vector Machine (LSVM), to efficiently learn the event in the derived subspace. The proposed GSDA-LSVM framework is used as an alternative to the Kernel Support Vector Machine (KSVM) approach, which despite its excellent classification accuracy requires significant computational resources for learning the events (i.e., for identifying the kernel parameters and KSVM penalty term) in large-scale video collections. In contrary, using the GSDA-LSVM approach the SVM penalty term can be rapidly identified in the lower dimensional subspace. Moreover, an additional speed up in deriving this lower-dimensional space is achieved by using the proposed GSDA method instead of conventional nonlinear subclass DA methods such as KSDA or KMSDA. This is made possible by GSDA exploiting the special structure of the inter-between-subclass scatter matrix to reformulate the original KSDA eigenvalue problem to one involving matrices of much smaller dimension. The proposed GSDA-LSVM approach leads to more accurate event detection and to computational efficiency gains, as shown by experimental results on the extensive TRECVID MED 2010 and 2012 datasets.