Search Contact information
University of Cambridge Home Department of Engineering
University of Cambridge > Engineering Department > Machine Intelligence Lab

Abstract for johnson_bmvc05

Proceedings of BMVC '05, 2005.

IMPROVED IMAGE ANNOTATION AND LABELLING THROUGH MULTI-LABEL BOOSTING

Matthew Johnson and Roberto Cipolla

September 5th, 2005

The majority of machine learning systems for object recognition is limited by their requirement of single labelled images for training, which are difficult to create or obtain in quantity. It is therefore impractical to use methods or techniques which require such data to build object recognizers for more than a relatively small subset of object classes. Instead, far more abundant multilabel data provides a ready means to create object recognition systems which are able to deal with large numbers of classes. In this paper we present a new object recognition system named MLBoost which learns from multi-label data through boosting and improves on state-of-the-art multi-label annotation and labelling systems. The system is trained on images with accompanying text and at no time is told which parts of each image correspond to which words, and as such the process is unsupervised. Having once been trained it is able to give segment labels and a list of descriptive words (an annotation) for any novel image.


(ftp:) johnson_bmvc05.pdf (http:) johnson_bmvc05.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.

© 2005 Cambridge University Engineering Dept
Information provided by milab-maintainer