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» Hierarchical Support Vector Random Fields: Joint Training to...
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ECCV
2008
Springer
14 years 7 months ago
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
Abstract. Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly e...
Paul Schnitzspan, Mario Fritz, Bernt Schiele
CLEF
2009
Springer
13 years 3 months ago
ImageCLEF 2009 Medical Image Annotation Task: PCTs for Hierarchical Multi-Label Classification
In this paper, we describe an approach for the automatic medical image annotation task of the 2009 CLEF cross-language image retrieval campaign (ImageCLEF). This work is focused o...
Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, ...
PAMI
2006
187views more  PAMI 2006»
13 years 5 months ago
An Experimental Study on Pedestrian Classification
Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; mul...
Stefan Munder, Dariu M. Gavrila
TMM
2010
270views Management» more  TMM 2010»
13 years 14 days ago
Sequence Multi-Labeling: A Unified Video Annotation Scheme With Spatial and Temporal Context
Abstract--Automatic video annotation is a challenging yet important problem for content-based video indexing and retrieval. In most existing works, annotation is formulated as a mu...
Yuanning Li, YongHong Tian, Ling-Yu Duan, Jingjing...
CVPR
2008
IEEE
14 years 7 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...