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» Learning Features by Contrasting Natural Images with Noise
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ICPR
2008
IEEE
14 years 9 months ago
A novel Gaussianized vector representation for natural scene categorization
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of featur...
Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang, ...
ICANN
2010
Springer
13 years 9 months ago
A Learned Saliency Predictor for Dynamic Natural Scenes
Abstract. We investigate the extent to which eye movements in natural dynamic scenes can be predicted with a simple model of bottom-up saliency, which learns on different visual re...
Eleonora Vig, Michael Dorr, Thomas Martinetz, Erha...
EACL
2006
ACL Anthology
13 years 9 months ago
Discriminative Sentence Compression with Soft Syntactic Evidence
We present a model for sentence compression that uses a discriminative largemargin learning framework coupled with a novel feature set defined on compressed bigrams as well as dee...
Ryan T. McDonald
SPIEVIP
2008
13 years 10 months ago
Adaptive methods of two-scale edge detection in post-enhancement visual pattern processing
Adaptive methods are defined and experimentally studied for a two-scale edge detection process that mimics human visual perception of edges and is inspired by the parvo-cellular (...
Zia-ur Rahman, Daniel J. Jobson, Glenn A. Woodell
CAIP
2001
Springer
293views Image Analysis» more  CAIP 2001»
14 years 10 days ago
A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features
In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian est...
Zoltan Kato, Ting-Chuen Pong