FBCC: An Image Similarity Algorithm Based on Regions

Authors:
Fang Qian;Lei Zhang;Fuzong Lin;Bo Zhang
Affiliations:
-;-;-;-
Venue:
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2001

Citing 8
Cited 0

VisualSEEk: a fully automated content-based image query system

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
WALRUS: a similarity retrieval algorithm for image databases

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: A System for Region-Based Image Indexing and Retrieval

VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Windsurf: Region-Based Image Retrieval Using Wavelets

DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Image Indexing Using Color Correlograms

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Relevance feedback: a power tool for interactive content-based image retrieval

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Region-based image retrieval has been an active research area for the past few years. A good similarity measure that combines information from all regions is very important for region-based retrieval systems. In this paper, we propose FBCC (Foreground-Background Corresponding Comparison), a novel image similarity measure based on region comparison. The basic idea is comparing query foreground regions with database foreground regions and query background regions with database background regions. Three factors have been considered in the algorithm: the comparable credit between two regions, the significance of each region and the difference of total number of regions between two images. Experimental results on a testbed of 10,000 general-purpose images show that this approach is effective for center-surround images.