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» Image Distance Using Hidden Markov Models
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CAE
2007
13 years 10 months ago
Extracting the Essence from Sets of Images
We use a set of photographs showing similar scenes as a model for a single photograph this scene. A distance measure for this model is defined by correlating the neigborhoods of p...
Marc Alexa
ICPR
2002
IEEE
14 years 9 months ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...
ECCV
2006
Springer
14 years 9 months ago
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection
This paper introduces the Located Hidden Random Field (LHRF), a conditional model for simultaneous part-based detection and segmentation of objects of a given class. Given a traini...
Ashish Kapoor, John M. Winn
CVIU
2007
113views more  CVIU 2007»
13 years 7 months ago
Primal sketch: Integrating structure and texture
This article proposes a generative image model, which is called ‘‘primal sketch,’’ following Marr’s insight and terminology. This model combines two prominent classes of...
Cheng-en Guo, Song Chun Zhu, Ying Nian Wu
CVPR
2004
IEEE
14 years 10 months ago
Learning Distance Functions for Image Retrieval
Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary ...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall