Temporal Multiple Correspondence Analysis For Big Data Mining In Soccer Videos

A multimedia big data mining framework consisting of two phases for interesting event detection in soccer videos has been proposed in this paper. In the pre-processing phase, it utilizes the multi-modal multi-filtering content analysis techniques for shot boundary detection and feature extraction. A pre-filtering process based on domain knowledge analysis is then applied to clean the noise and obtain a candidate set. In the event detection phase, a temporal multiple correspondence analysis (TMCA) algorithm that adopts an indicator weighting scheme is proposed to efficiently and effectively incorporate the temporal semantic information for improving the detection results. Furthermore, another enhanced MCA (EN-MCA) approach is presented to better capture the correspondence between feature items and classes by thoroughly utilizing the pair-wise principal components. Finally, a re-ranking procedure is performed to retrieve the missed interesting event. Our proposed semantic re-ranking framework is evaluated on a large collection of soccer videos for interesting event detection. The experimental results demonstrate the effectiveness of the proposed framework.