Sciweavers

13 search results - page 1 / 3
» Query Decomposition: A Multiple Neighborhood Approach to Rel...
Sort
View
ICDE
2006
IEEE
191views Database» more  ICDE 2006»
14 years 5 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
SIGMOD
2003
ACM
237views Database» more  SIGMOD 2003»
14 years 3 months ago
Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval
The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance ...
Deok-Hwan Kim, Chin-Wan Chung
ICMCS
2000
IEEE
170views Multimedia» more  ICMCS 2000»
13 years 8 months ago
Update Relevant Image Weights for Content-Based Image Retrieval using Support Vector Machines
Relevance feedback [1] has been a powerful tool for interactive Content-Based Image Retrieval (CBIR). During the retrieval process, the user selects the most relevant images and p...
Qi Tian, Pengyu Hong, Thomas S. Huang
SPIESR
1998
195views Database» more  SPIESR 1998»
13 years 5 months ago
Relevance Feedback Techniques in Interactive Content-Based Image Retrieval
Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many system...
Yong Rui, Thomas S. Huang, Sharad Mehrotra
ICMCS
2005
IEEE
221views Multimedia» more  ICMCS 2005»
13 years 9 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...