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» Unsupervised Image Layout Extraction
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ICPR
2006
IEEE
14 years 6 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
ICDM
2006
IEEE
164views Data Mining» more  ICDM 2006»
13 years 11 months ago
Unsupervised Learning of Tree Alignment Models for Information Extraction
We propose an algorithm for extracting fields from HTML search results. The output of the algorithm is a database table– a data structure that better lends itself to high-level...
Philip Zigoris, Damian Eads, Yi Zhang
ICDAR
2007
IEEE
13 years 7 months ago
Example-Based Logical Labeling of Document Title Page Images
This paper presents a flexible and effective examplebased approach for labeling title pages which can be used for automated extraction of bibliographic data. The labels of intere...
Joost van Beusekom, Daniel Keysers, Faisal Shafait...
ICDAR
2009
IEEE
13 years 2 months ago
Unconstrained Handwritten Document Layout Extraction Using 2D Conditional Random Fields
The paper describes a new approach using a Conditional Random Fields (CRFs) to extract physical and logical layouts in unconstrained handwritten letters such as those sent by indi...
Florent Montreuil, Emmanuele Grosicki, Laurent Heu...
ICIP
2000
IEEE
14 years 6 months ago
Unsupervised Color Texture Feature Extraction and Selection for Soccer Image Segmentation
In this paper, we describe a new approach for color texture featureextraction and selection. We definecolortexturefeatures as texture features which are computed by taking into ac...
Nicolas Vandenbroucke, Ludovic Macaire, Jack-G&eac...