DocumentCode :
2307713
Title :
Definition of descriptors for semantic image interpretation
Author :
Ivasic-Kos, Marina ; Poscic, Patrizia ; Pavlic, Mile
Author_Institution :
Dept. for Comput. Sci., Univ. in Rijeka, Rijeka, Croatia
fYear :
2010
fDate :
5-6 July 2010
Firstpage :
214
Lastpage :
219
Abstract :
A lot of effort has been put into researching image interpretation, but there is still no universally accepted approach to map low-level feature into high level image semantic interpretation. In this paper, a method for continuous low-level features vector quantization is presented so as to define appropriate values for descriptive variables. The similarity among different concepts of the domain is examined and compared by using the measure of similarity which is based on the probabilistic model and the measure of distance. Also, an abstract image description vector suitable for image analysis is given.
Keywords :
content-based retrieval; feature extraction; image retrieval; probability; vector quantisation; abstract image description vector; content based image retrieval; descriptive variables; high level semantic image interpretation; image analysis; low-level feature vector quantization; probabilistic model; image classification; image representations; quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Visual Information Processing (EUVIP), 2010 2nd European Workshop on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-7288-8
Type :
conf
DOI :
10.1109/EUVIP.2010.5699133
Filename :
5699133
Link To Document :
بازگشت