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» Learning Iterative Image Reconstruction
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CVPR
2012
IEEE
13 years 6 days ago
Exploiting local and global patch rarities for saliency detection
We introduce a saliency model based on two key ideas. The first one is considering local and global image patch rarities as two complementary processes. The second one is based o...
Ali Borji, Laurent Itti
CIVR
2008
Springer
222views Image Analysis» more  CIVR 2008»
14 years 11 months ago
Automatic image annotation via local multi-label classification
As the consequence of semantic gap, visual similarity does not guarantee semantic similarity, which in general is conflicting with the inherent assumption of many generativebased ...
Mei Wang, Xiangdong Zhou, Tat-Seng Chua
MIR
2005
ACM
140views Multimedia» more  MIR 2005»
15 years 3 months ago
Multiple random walk and its application in content-based image retrieval
In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by mea...
Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying M...
CVPR
2009
IEEE
15 years 1 months ago
Imbalanced RankBoost for efficiently ranking large-scale image/video collections
Ranking large scale image and video collections usually expects higher accuracy on top ranked data, while tolerates lower accuracy on bottom ranked ones. In view of this, we propo...
Michele Merler, Rong Yan, John R. Smith
CVPR
2009
IEEE
16 years 4 months ago
Learning a Distance Metric from Multi-instance Multi-label Data
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. ...
Rong Jin (Michigan State University), Shijun Wang...