As image collections become ever larger, effective access to their content requires a meaningful categorization of the images. Such a categorization can rely on clustering methods working on image features, but should greatly benefit from any form of supervision the user can provide, related to the visual content. Semi-supervised clustering - learning from both labelled and unlabelled data - has consequently become a topic of significant interest. In this paper we present a new semi-supervised clustering algorithm, pairwise-constrained competitive agglomeration, which is based on a fuzzy cost function that takes pairwise constraints into account.
Semi-supervised image database categorization using pairwise constraints
IEEE International Conference on Image Processing 2005 ; 3 ; III-1228
2005-01-01
196771 byte
Conference paper
Electronic Resource
English
Semi-Supervised Image Database Categorization using Pairwise Constraintsv
British Library Conference Proceedings | 2005
|Active Image Clustering with Pairwise Constraints from Humans
British Library Online Contents | 2014
|On the use of supervised features for unsupervised image categorization: An evaluation
British Library Online Contents | 2014
|Experiments on Supervised Learning Algorithms for Text Categorization
British Library Conference Proceedings | 2005
|