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» Active Learning Methods for Interactive Image Retrieval
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ICPR
2000
IEEE
16 years 3 months ago
Image Distance Using Hidden Markov Models
We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its stati...
Daniel DeMenthon, David S. Doermann, Marc Vuilleum...
ICMCS
2006
IEEE
174views Multimedia» more  ICMCS 2006»
15 years 8 months ago
Web Image Mining Based on Modeling Concept-Sensitive Salient Regions
In this paper, we propose a probabilistic model for web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-...
Jing Liu, Qingshan Liu, Jinqiao Wang, Hanqing Lu, ...
DAS
2010
Springer
15 years 6 months ago
Towards more effective distance functions for word image matching
Matching word images has many applications in document recognition and retrieval systems. Dynamic Time Warping (DTW) is popularly used to estimate the similarity between word imag...
Raman Jain, C. V. Jawahar
TKDE
2008
116views more  TKDE 2008»
15 years 1 months ago
Long-Term Cross-Session Relevance Feedback Using Virtual Features
Relevance feedback (RF) is an iterative process, which refines the retrievals by utilizing the user's feedback on previously retrieved results. Traditional RF techniques solel...
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei...
HISB
2011
92views more  HISB 2011»
14 years 1 months ago
Unsupervised Grow-Cut: Cellular Automata-Based Medical Image Segmentation
— This paper presents a new cellular automata-based unsupervised image segmentation technique that is motivated by the interactive grow-cut algorithm. In contrast to the traditio...
Payel Ghosh, Sameer Antani, L. Rodney Long, George...