~ Lexing
Xie / Research
/ ObjRecg |
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Semi-supervised Learning of Patch Based Appearance
Model
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Overview |
This work addresses the problem of learning
object appearance from image patches under a semi-supervised scenario.
We develop an original version of Gaussian mixture model (GMM) learning
algorithm based on partially labeled data; we also propose techniques
for saliency-based ranking and selection of the learned mixture components.
Experiments show effective yet fast learning in identifying salient patches
in the appearance of a class of objects. This work has promising extensions
for both advancing state-of-the-art part-based object detectors, and incorporating
partial knowledge into multimedia analysis systems in a principled manner. |
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Publications and Reports |
L. Xie,
P. Pérez (2004). Slightly
Supervised Learning of Part-Based Appearance Models, IEEE Workshop
on Learning in Computer Vision and Pattern Recognition, in conjunction
with CVPR 2004, Washington DC, June 2004 (PDF,
Slides) |
Last update:
August 5, 2004
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