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CVPR
2012
IEEE
9 years 3 months ago
Unsupervised feature learning framework for no-reference image quality assessment
In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to...
Peng Ye, Jayant Kumar, Le Kang, David S. Doermann
ICASSP
2011
IEEE
10 years 5 months ago
Semi-supervised handwritten digit recognition using very few labeled data
We propose a novel semi-supervised classifier for handwritten digit recognition problems that is based on the assumption that any digit can be obtained as a slight transformation...
Steven Van Vaerenbergh, Ignacio Santamaría,...
NIPS
1998
11 years 2 months ago
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
This paper presents probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored...
Shumeet Baluja
CVPR
2010
IEEE
11 years 9 months ago
Collect-Cut: Segmentation with Top-Down Cues Discovered in Multi-Object Images
We present a method to segment a collection of unlabeled images while exploiting automatically discovered appearance patterns shared between them. Given an unlabeled pool of multi...
Yong Jae Lee, Kristen Grauman
ICML
2007
IEEE
12 years 2 months ago
Self-taught learning: transfer learning from unlabeled data
We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabele...
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin ...
CVPR
2007
IEEE
12 years 3 months ago
Learning Visual Representations using Images with Captions
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
Ariadna Quattoni, Michael Collins, Trevor Darrell
CVPR
2006
IEEE
12 years 3 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell
CVPR
2004
IEEE
12 years 3 months ago
Is Bottom-Up Attention Useful for Object Recognition?
A key problem in learning multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which...
Ueli Rutishauser, Dirk Walther, Christof Koch, Pie...
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