PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unsupervised Object Discovery: A Comparison
Tinne Tuytelaars, Christoph Lampert, Matthew Blaschko and Wray Buntine
International Journal of Computer Vision (IJCV) 2009. ISSN 0920-5691 (Print) 1573-1405 (Online)


The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5517
Deposited By:Karteek Alahari
Deposited On:30 December 2009