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IDEAL
2005
Springer
14 years 27 days ago
Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval
It is known that no single descriptor is powerful enough to encompass all aspects of image content, i.e. each feature extraction method has its own view of the image content. A pos...
Lin Mei, Gerd Brunner, Lokesh Setia, Hans Burkhard...
ICMCS
2005
IEEE
221views Multimedia» more  ICMCS 2005»
14 years 1 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 7 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»
13 years 11 months 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...
EMNLP
2006
13 years 8 months ago
Relevance Feedback Models for Recommendation
We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the anal...
Masao Utiyama, Mikio Yamamoto