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Proceedings Paper

Classifying images using multiple binary-class decision trees for object-based image retrieval
Author(s): Linhui Jia; Leslie Kitchen
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Paper Abstract

This paper describes an approach to multiclass object classification using local information based invariant object-contour representation and a combination of one-per- class binary-class decision tree classifiers. The object representation scheme is based on the polygonal approximations of object contours. C4.5 is used to learn each of the binary-class tree classifiers which are used to predict the class of each segment of an object. A new decision combination method is used to determine the class of an object based on class probability distribution of each segment of the object on each of the binary-class trees. The proposed object classification approach is invariant to translation, rotation, and scale changes of objects. On applying this approach to a hand tool image database in the situation of image retrieval, the experimental results show that the retrieval performance is significantly better than the results obtained by previous studies.

Paper Details

Date Published: 27 December 2000
PDF: 9 pages
Proc. SPIE 4311, Internet Imaging II, (27 December 2000); doi: 10.1117/12.411889
Show Author Affiliations
Linhui Jia, Univ. of Melbourne (Australia)
Leslie Kitchen, Univ. of Melbourne (Australia)


Published in SPIE Proceedings Vol. 4311:
Internet Imaging II
Giordano B. Beretta; Raimondo Schettini, Editor(s)

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