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ICMCS
2005
IEEE
221views Multimedia» more  ICMCS 2005»
14 years 2 months ago
A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...
MTA
2008
146views more  MTA 2008»
13 years 8 months ago
A survey of browsing models for content based image retrieval
The problem of content based image retrieval (CBIR) has traditionally been investigated within a framework that emphasises the explicit formulation of a query: users initiate an au...
Daniel Heesch
ICMCS
2000
IEEE
116views Multimedia» more  ICMCS 2000»
14 years 29 days ago
Non-linear Relevance Feedback: Improving the Performance of Content-Based Retrieval Systems
In this paper, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of content-based retrieval systems. In particular, the huma...
Nikolaos D. Doulamis, Anastasios D. Doulamis, Stef...
ICDE
2006
IEEE
191views Database» more  ICDE 2006»
14 years 10 months ago
Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval
Today's Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (kNN) model. They retrieve images from a single neighborhood using lo...
Kien A. Hua, Ning Yu, Danzhou Liu
CVPR
2006
IEEE
14 years 10 months ago
A Simple Bayesian Framework for Content-Based Image Retrieval
We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified ...
Katherine A. Heller, Zoubin Ghahramani