DocumentCode :
341886
Title :
Classification-driven object-based image retrieval
Author :
Jia, Linhui ; Kitchen, Leslie
Author_Institution :
Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Parkville, Vic., Australia
Volume :
1
fYear :
1999
fDate :
36342
Firstpage :
618
Abstract :
This paper describes an approach for object-based image retrieval based on classes of objects in images. In this approach, contours of objects are extracted from images and are represented under a scheme which satisfies scale, rotation and translation invariance. Classifier learning techniques are used to classify objects in images into different classes. Image similarity calculation is performed based on class information of objects. Experimental results show that the method is effective and efficient
Keywords :
image classification; image representation; image retrieval; multimedia databases; visual databases; classifier learning techniques; experimental results; image classification; image representation; image similarity calculation; object contours; object-based image retrieval; rotation invariance; scale invariance; translation invariance; Classification tree analysis; Computer science; Computer vision; Decision trees; Image databases; Image retrieval; Image segmentation; Machine intelligence; Object recognition; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Computing and Systems, 1999. IEEE International Conference on
Conference_Location :
Florence
Print_ISBN :
0-7695-0253-9
Type :
conf
DOI :
10.1109/MMCS.1999.779271
Filename :
779271
Link To Document :
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