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ICMCS
2005
IEEE
221views Multimedia» more  ICMCS 2005»
15 years 3 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...
ICDE
2006
IEEE
191views Database» more  ICDE 2006»
15 years 11 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
AIRS
2009
Springer
15 years 1 months ago
Enabling Effective User Interactions in Content-Based Image Retrieval
Abstract. This paper presents an interactive content-based image retrieval framework--uInteract, for delivering a novel four-factor user interaction model visually. The four-factor...
Haiming Liu 0002, Srdan Zagorac, Victoria S. Uren,...
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ICMCS
2000
IEEE
142views Multimedia» more  ICMCS 2000»
15 years 2 months ago
Incorporate Discriminant Analysis with EM Algorithm in Image Retrieval
One of the difficulties of Content-Based Image Retrieval (CBIR) is the gap between high-level concepts and low-level image features, e.g., color and texture. Relevance feedback wa...
Qi Tian, Ying Wu, Thomas S. Huang
ICIP
1999
IEEE
15 years 11 months ago
Water-Filling: A Novel Way for Image Structural Feature Extraction
The performance of a content based image retrieval (CBIR) system is inherently constrained by the features adopted to represent the images in the database. In this paper, a new ap...
Xiang Sean Zhou, Yong Rui, Thomas S. Huang