Improved Image Annotation and Labelling through Multi-Label Boosting
Matthew Johnson, Roberto Cipolla · 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.