Video event detection using a subclass recoding error-correcting output codes framework

In this paper, complex video events are learned and detected using a novel subclass recoding error-correcting outputs (SRECOC) design. In particular, a set of pre-trained concept detectors along different low-level visual feature types are used to provide a model vector representation of video signals. Subsequently, a subclass partitioning algorithm is used to divide only the target event class to several subclasses and learn one subclass detector for each event subclass. The pool of the subclass detectors is then combined under a SRECOC framework to provide a single event detector. This is achieved by first exploiting the properties of the linear loss-weighted decoding measure in order to derive a probability estimate along the different event subclass detectors, and then utilizing the sum probability rule along event subclasses to retrieve a single degree of confidence for the presence of the target event in a particular test video. Experimental results on the large-scale video collections of the TRECVID Multimedia Event Detection (MED) task verify the effectiveness of the proposed method. Moreover, the effect of weak or strong concept detectors on the accuracy of the resulting event detectors is examined.