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» Pruning Training Sets for Learning of Object Categories
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CVPR
2007
IEEE
14 years 9 months ago
Semantic Hierarchies for Visual Object Recognition
In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior kno...
Marcin Marszalek, Cordelia Schmid
ICIP
2007
IEEE
14 years 9 months ago
Fast Detection of Independent Motion in Crowds Guided by Supervised Learning
Different from appearance-based methods, clustering feature points only by their motion coherence is an emerging category of approach to detecting and tracking individuals among c...
Yuan Li, Haizhou Ai
IJCV
2008
223views more  IJCV 2008»
13 years 7 months ago
Robust Object Detection with Interleaved Categorization and Segmentation
This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure...
Bastian Leibe, Ales Leonardis, Bernt Schiele
CVPR
2008
IEEE
14 years 9 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
IJCV
2008
192views more  IJCV 2008»
13 years 7 months ago
Learning to Locate Informative Features for Visual Identification
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...