Classifying images using multiple binary-class decision trees for object-based image retrieval

Linhui Jia; Leslie Kitchen

doi:10.1117/12.411889

27 December 2000 Classifying images using multiple binary-class decision trees for object-based image retrieval

Linhui Jia, Leslie Kitchen

Proceedings Volume 4311, Internet Imaging II; (2000) https://doi.org/10.1117/12.411889
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

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.

Citation Download Citation

Linhui Jia and Leslie Kitchen "Classifying images using multiple binary-class decision trees for object-based image retrieval", Proc. SPIE 4311, Internet Imaging II, (27 December 2000); https://doi.org/10.1117/12.411889

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available