DocumentCode :
3284779
Title :
Video object encoder using selective local-space support vector machines
Author :
Tsai, Po Hsiang ; Jan, Seun ; Gunes, Hatice
Author_Institution :
Fac. of Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
fYear :
2004
fDate :
29 Sept.-1 Oct. 2004
Firstpage :
427
Lastpage :
429
Abstract :
Recently, support vector machine (SVM) has been shown to be a good classifier; however, its large computational requirement prohibited its use in real time video processing applications. In this paper, a model is proposed that enables use of SVM in video applications. The proposed model allows selected image scales (of interest) to be encoded and classified more accurately by complex classifier such as SVM, whilst other image scales of less significance to be encoded and classified by simpler encoder/classifier. Experiment with video object encoding shows that the performance of the proposed model is comparable with other models, however with reduced computational requirements.
Keywords :
computational complexity; image classification; support vector machines; video coding; complex classifier; image scale; real time video processing application; selective local-space support vector machine; video object encoder; Australia; Encoding; Image analysis; Image classification; Image processing; Object oriented modeling; Support vector machine classification; Support vector machines; Telephony; Video compression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Signal Processing, 2004 IEEE 6th Workshop on
Print_ISBN :
0-7803-8578-0
Type :
conf
DOI :
10.1109/MMSP.2004.1436584
Filename :
1436584
Link To Document :
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