DMCA
CBSA: Content-Based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines (2003)
Download From
IEEE
Download Links
- [www.mmdb.ece.ucsb.edu]
- [www-db.stanford.edu]
- [infolab.stanford.edu]
- [infolab.stanford.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | IEEE Transactions on Circuits and Systems for Video Technology |
| Citations: | 143 - 8 self |
BibTeX
@ARTICLE{Chang03cbsa:content-based,
author = {Edward Chang and Gerard Sychay},
title = {CBSA: Content-Based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
year = {2003},
volume = {13},
pages = {26--38}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
We propose a content-based soft annotation (CBSA) procedure for providing images with semantical labels. The annotation procedure starts with labeling a small set of training images, each with one single semantical label (e.g., forest, animal, or sky). An ensemble of binary classifiers is then trained for predicting label membership for images. The trained ensemble is applied to each individual image to give the image multiple soft labels, and each label is associated with a label membership factor. To select a base binary-classifier for CBSA, we experiment with two learning methods, Support Vector Machines (SVMs) and Bayes Point Machines (BPMs), and compare their class-prediction accuracy. Our empirical study on a 116-category 25K-image set shows that the BPM-based ensemble provides better annotation quality than the SVM-based ensemble for supporting multimodal image retrievals.
Keyphrases
content-based soft annotation image multiple soft label 116-category 25k-image set show individual image small set svm-based ensemble trained ensemble multimodal image retrieval single semantical label semantical label base binary-classifier support vector machine class-prediction accuracy label membership factor label membership binary classifier annotation procedure empirical study training image bayes point machine annotation quality
