Content Based Image Retrieval through Object Extraction and Querying

Authors:
A. H. Kam;T. T. Ng;N. G. Kingsbury;W. J. Fitzgerald
Affiliations:
-;-;-;-
Venue:
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Year:
2000

Citing 0
Cited 8

A Method for Evaluating the Performance of Content-Based Image Retrieval Systems Based on Subjectively Determined Similarity between Images

CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Constraint Based Region Matching for Image Retrieval

International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Improving retrieval performance by region constraints and relevance feedback

Journal of Computer Science and Technology
Query by example using invariant features from the double dyadic dual-tree complex wavelet transform

Proceedings of the ACM International Conference on Image and Video Retrieval
Central object extraction for object-based image retrieval

CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform

Image and Vision Computing
Extracting moving / static objects of interest in video

PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Color image segmentation based on the block homogeneity

ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a content-based image retrieval system based on object extraction through image segmentation. A general and powerful multiscale segmentation algorithm automates the segmentation process, the output of which is assigned novel color and texture descriptors, which are both efficient and effective. Query strategies consisting of a semi-automated and a fully automated mode are developed which are shown to produce good results. We then show the superiority of our approach over the global histogram approach, which proves that the ability to access images at the level of objects is essential for CBIR.