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» Learning from Ambiguously Labeled Images
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NIPS
2001
13 years 9 months ago
Unsupervised Learning of Human Motion Models
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Yang Song, Luis Goncalves, Pietro Perona
CVPR
2005
IEEE
14 years 10 months ago
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multi...
Jinbo Bi, Yixin Chen, James Ze Wang
CVPR
2009
IEEE
15 years 3 months ago
Contextual Classification with Functional Max-Margin Markov Networks
We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To...
Daniel Munoz, James A. Bagnell, Martial Hebert, Ni...
SIGIR
2005
ACM
14 years 2 months ago
A database centric view of semantic image annotation and retrieval
We introduce a new model for semantic annotation and retrieval from image databases. The new model is based on a probabilistic formulation that poses annotation and retrieval as c...
Gustavo Carneiro, Nuno Vasconcelos
CVPR
2009
IEEE
15 years 3 months ago
Shared Kernel Information Embedding for Discriminative Inference
Latent Variable Models (LVM), like the Shared-GPLVM and the Spectral Latent Variable Model, help mitigate over- fitting when learning discriminative methods from small or modera...
David J. Fleet, Leonid Sigal, Roland Memisevic