Title :
L-GEM based RBFNN for news anchorperson detection with Dominant Color Descriptor
Author :
Xiao, Wei ; Ng, Wing W Y ; Chan, Patrick P K ; Yeung, Daniel S.
Author_Institution :
Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China
Abstract :
News reports in TV provide important and timely information about the city and the world to citizens. Moreover, data mining and indexing of news video clips provide a good source of information. However, news video usually consists of more than one news story. One must split them into individual news before indexing. Owing to the nature of news reports, the news anchorperson usually appears in the transition of two news story. Therefore, we propose a new method to find the news anchorperson shot in news video. The MPEG-7 Dominant Color Descriptor (DCD) is adopted to describe video frames. Radial Basis Function Neural Network (RBFNN) trained by minimizing the Localized Generalization Error (L-GEM) is adopted to classify the occurrence of news anchorperson in video frames. Experimental results show that the proposed method is accurate for different news videos from different TV stations.
Keywords :
image classification; image colour analysis; radial basis function networks; video signal processing; DCD; L-GEM; MPEG-7 dominant color descriptor; RBFNN; data mining; dominant color descriptor; localized generalization error; news anchorperson detection; radial basis function neural network; Biomedical monitoring; Biosensors; Image color analysis; TV; Temperature measurement; Temperature sensors; Training; Anchorperson; L-GEM; News video; RBFNN;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580574
