Title :
Scenery video type classification based on SVM
Author_Institution :
Comput. Sch., Inner Mongolia Normal Univ., Huhhot, China
Abstract :
In this paper SVM algorithm is applied to classify the scenery video types in compressed domain. Firstly we extract video sequences randomly from scenery video and detect representative frames from the video sequences; secondly we extract features such as color layout, dominant color, edge histogram and face feature; then according to SVM, representative frames are classified as natural scenery, personality, animal and plant. Experimental results have shown that the result of our algorithm is very high accuracy.
Keywords :
data compression; edge detection; feature extraction; image classification; image colour analysis; image sequences; support vector machines; video coding; video signal processing; color layout; dominant color; edge histogram; face feature; representative frames detection; scenery video type classification; support vector machine; video sequences extraction; Animals; Decoding; Face detection; Feature extraction; Histograms; Support vector machine classification; Support vector machines; Training data; Video compression; Video sequences;
Conference_Titel :
Microelectronics & Electronics, 2009. PrimeAsia 2009. Asia Pacific Conference on Postgraduate Research in
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4668-1
Electronic_ISBN :
978-1-4244-4669-8
DOI :
10.1109/PRIMEASIA.2009.5397389
