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ECCV
2008
Springer
11 years 3 months ago
Weakly Supervised Object Localization with Stable Segmentations
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
PAMI
2006
185views more  PAMI 2006»
10 years 1 months ago
Generic Object Recognition with Boosting
This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of e...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
BVAI
2005
Springer
10 years 6 months ago
Learning Location Invariance for Object Recognition and Localization
A visual system not only needs to recognize a stimulus, it also needs to find the location of the stimulus. In this paper, we present a neural network model that is able to genera...
Gwendid T. van der Voort van der Kleij, Frank van ...
ICCV
2005
IEEE
11 years 3 months ago
Object Recognition in High Clutter Images Using Line Features
We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Finding correspondences between model an...
Philip David, Daniel DeMenthon
ECCV
2008
Springer
11 years 3 months ago
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
Abstract. The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often "engineered" to...
Andrew Gilbert, John Illingworth, Richard Bowden
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