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» Learning Over Multiple Temporal Scales in Image Databases
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ECCV
2000
Springer
16 years 3 days ago
Learning Over Multiple Temporal Scales in Image Databases
Abstract. The ability to learn from user interaction is an important asset for content-based image retrieval (CBIR) systems. Over short times scales, it enables the integration of ...
Nuno Vasconcelos, Andrew Lippman
KDD
1997
ACM
184views Data Mining» more  KDD 1997»
15 years 2 months ago
JAM: Java Agents for Meta-Learning over Distributed Databases
In this paper, we describe the JAM system, a distributed, scalable and portable agent-based data mining system that employs a general approach to scaling data mining applications ...
Salvatore J. Stolfo, Andreas L. Prodromidis, Shell...
102
Voted
CVPR
2004
IEEE
16 years 8 days ago
High-Zoom Video Hallucination by Exploiting Spatio-Temporal Regularities
In this paper, we consider the problem of super-resolving a human face video by a very high (?16) zoom factor. Inspired by recent literature on hallucination and examplebased lear...
Göksel Dedeoglu, Jonas August, Takeo Kanade
92
Voted
ECCV
2004
Springer
16 years 4 days ago
Steering in Scale Space to Optimally Detect Image Structures
Detecting low-level image features such as edges and ridges with spatial filters is improved if the scale of the features are known a priori. Scale-space representations and wavele...
Jeffrey Ng, Anil A. Bharath
92
Voted
ICIP
2007
IEEE
15 years 12 months ago
Large Scale Learning of Active Shape Models
We propose a framework to learn statistical shape models for faces as piecewise linear models. Specifically, our methodology builds upon primitive active shape models(ASM) to hand...
Atul Kanaujia, Dimitris N. Metaxas