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» An active feedback framework for image retrieval
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KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 7 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
SIGIR
2005
ACM
14 years 28 days ago
Personalizing search via automated analysis of interests and activities
We formulate and study search algorithms that consider a user’s prior interactions with a wide variety of content to personalize that user’s current Web search. Rather than re...
Jaime Teevan, Susan T. Dumais, Eric Horvitz
CVPR
2007
IEEE
13 years 11 months ago
Diverse Active Ranking for Multimedia Search
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
ShyamSundar Rajaram, Charlie K. Dagli, Nemanja Pet...
ICMCS
2006
IEEE
155views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Region-Based Image Retrieval using Radial Basis Function Network
This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified u...
Kui Wu, Kim-Hui Yap, Lap-Pui Chau
CVPR
2008
IEEE
14 years 9 months ago
Semi-supervised distance metric learning for Collaborative Image Retrieval
Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, rele...
Steven C. H. Hoi, Wei Liu, Shih-Fu Chang