A novel region-based image retrieval method using relevance feedback

Authors:
Feng Jing;Bo Zhang;Fuzong Lin;Wei-Ying Ma;Hong-Jiang Zhang
Affiliations:
Tsinghua Univ., Beijing, China;Tsinghua Univ., Beijing, China;Tsinghua Univ., Beijing, China;Microsoft Research, Beijing, China;Microsoft Research, Beijing, China
Venue:
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Year:
2001

Citing 4
Cited 8

VisualSEEk: a fully automated content-based image query system

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
IRM: integrated region matching for image retrieval

MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

IEEE Transactions on Pattern Analysis and Machine Intelligence
NeTra: a toolbox for navigating large image databases

ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1

An effective region-based image retrieval framework

Proceedings of the tenth ACM international conference on Multimedia
The Truth about Corel - Evaluation in Image Retrieval

CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Semantic-meaningful content-based image retrieval in wavelet domain

MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Content-based sub-image retrieval using relevance feedback

Proceedings of the 2nd ACM international workshop on Multimedia databases
SmartLabel: an object labeling tool using iterated harmonic energy minimization

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semi-automatically labeling objects in images

IEEE Transactions on Image Processing
Unbalanced region matching based on two-level description for image retrieval

Pattern Recognition Letters
Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging

ACM Transactions on Interactive Intelligent Systems (TiiS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Content-based image retrieval using region segmentation has been an active research area in the past few years. Constrasting to traditional approaches, which compute only global features of images, the region-based methods extract features of the segmented regions and perform similarity comparisons at the granularity of region. In this paper, we propose a novel region-based retrieval method, Self-Learned Region Importance (SLRI). In this method, image similarity measure is based on the region importance learned from users' feedback. The region importance that coincides that human perception con not only be used in a query session, but also be memorized and cumulated for future queries. Experimental results on a database of about 8,600 general-purposed images show the effectiveness of our method using relevance feedback.