Unsupervised Object Discovery: A Comparison
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...[Show more]
|Collections||ANU Research Publications|
|Source:||International Journal of Computer Vision|
|01_Tuytelaars_Unsupervised_Object_Discovery:_2010.pdf||5.27 MB||Adobe PDF||Request a copy|
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