Title :
A TV-logo classification and learning system
Author :
Nieto, P. ; Cózar, J.R. ; González-Linares, J.M. ; Guil, N.
Author_Institution :
Dept. of Comput. Archit., Univ. of Malaga, Malaga
Abstract :
Logotypes superimposed to broadcasted videos supply important information for semantic video annotation, such as the content creator. In this work a novel logo classification and learning system for TV broadcast videos is presented. Logos are segmented from the video stream but scale change, position shift, clutter and noise makes difficult to classify and to recognize them. Several robust features that use edges and shape information have been selected, and a Bayesian network classifier is used to classify the logos. New logos are recognized as such for the first time they appear and passed to a semi-supervised learning system. The learning process clusters the set of new logos to group different instances of the same new logo. A logo model is obtained for each cluster that must be validated by a human to incorporate them into the classification system. Comprehensive tests with a set of 724 TV logos show the high performance of our classification and learning system.
Keywords :
Bayes methods; image classification; learning (artificial intelligence); television broadcasting; video signal processing; video streaming; Bayesian network classifier; TV broadcast videos; TV-logo classification; broadcasted videos; learning process; logotypes; semantic video annotation; semisupervised learning system; video stream; Bayesian methods; Learning systems; Multimedia communication; Noise robustness; Noise shaping; Semisupervised learning; Shape; Streaming media; TV broadcasting; Videos; Bayesian network classifier; Clustering; Logo classification; Logo learning;
Conference_Titel :
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-1765-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2008.4712313
