Object-based image similarity computation using inductive learning of contour-segment relations

Author

Jia, Linhui ; Kitchen, Leslie

Author_Institution

Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

9

Issue

1

fYear

2000

fDate

1/1/2000 12:00:00 AM

Firstpage

80

Lastpage

87

Abstract

Describes an efficient and effective image similarity calculation method for object-based image comparison at the level of object classes. It uses probabilistic-prediction voting based on the predicted class distribution of each segment of the contour of an object in an image to determine the class of the object. The C4.5 inductive learning algorithm is used to predict the class distribution of object-contour segments. This method is invariant to rotation, scaling and translation of objects. Experimental results show that the method is effective and efficient. It can be used for object-based image retrieval

Keywords

edge detection; image classification; image matching; image retrieval; image segmentation; learning by example; object recognition; probability; C4.5 inductive learning algorithm; class distribution prediction; contour-segment relations; image similarity calculation method; object classes; object-based image comparison; object-based image retrieval; object-based image similarity computation; object-contour segments; probabilistic-prediction voting; rotation invariance; scaling invariance; translation invariance; Computer applications; Euclidean distance; Image retrieval; Image segmentation; Layout; Prototypes; Shape measurement; Software prototyping; Supervised learning; Voting;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.817600

Filename

817600