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» Geo-located image analysis using latent representations
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ICPR
2006
IEEE
14 years 5 months ago
Latent Layout Analysis for Discovering Objects in Images
Latent Layout Analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative ...
David Liu, Datong Chen, Tsuhan Chen
CVPR
2008
IEEE
14 years 6 months ago
Geo-located image analysis using latent representations
Image categorization is undoubtedly one of the most challenging open problems faced in Computer Vision, far from being solved by employing pure visual cues. Recently, additional t...
Marco Cristani, Alessandro Perina, Umberto Castell...
CVPR
2011
IEEE
13 years 2 months ago
Learning Better Image Representations Using 'Flobject Analysis'
Unsupervised learning can be used to extract image representations that are useful for various and diverse vision tasks. After noticing that most biological vision systems for int...
Inmar Givoni, Patrick Li, Brendan Frey
MMM
2011
Springer
368views Multimedia» more  MMM 2011»
12 years 8 months ago
Correlated PLSA for Image Clustering
Probabilistic Latent Semantic Analysis (PLSA) has become a popular topic model for image clustering. However, the traditional PLSA method considers each image (document) independen...
Peng Li, Jian Cheng, Zechao Li, Hanqing Lu
ICPR
2010
IEEE
13 years 2 months ago
Scene Classification Using Spatial Pyramid of Latent Topics
We propose a scene classification method, which combines two popular methods in the literature: Spatial Pyramid Matching (SPM) and probabilistic Latent Semantic Analysis (pLSA) mod...
Emrah Ergul, Nafiz Arica