Efficient and Flexible Cluster-and-Search for CBIR

Authors:
Anderson Rocha;Jurandy Almeida;Mario A. Nascimento;Ricardo Torres;Siome Goldenstein
Affiliations:
Institute of Computing, University of Campinas, Brazil;Institute of Computing, University of Campinas, Brazil;Department of Computing Science, University of Alberta, Canada;Institute of Computing, University of Campinas, Brazil;Institute of Computing, University of Campinas, Brazil
Venue:
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Year:
2008

Citing 9
Cited 3

Color indexing

International Journal of Computer Vision
Comparing images using color coherence vectors

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Data mining: concepts and techniques

Data mining: concepts and techniques
A compact and efficient image retrieval approach based on border/interior pixel classification

Proceedings of the eleventh international conference on Information and knowledge management
An Adaptive and Efficient Clustering-Based Approach for Content-Based Image Retrieval in Image Databases

IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
Clustering and Information Retrieval (Network Theory and Applications)

Clustering and Information Retrieval (Network Theory and Applications)
A unified framework for image database clustering and content-based retrieval

Proceedings of the 2nd ACM international workshop on Multimedia databases
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Hierarchical partitions for content image retrieval from large-scale database

MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition

BP-tree: an efficient index for similarity search in high-dimensional metric spaces

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A scalable re-ranking method for content-based image retrieval

Information Sciences: an International Journal
Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Content-Based Image Retrieval is a challenging problem both in terms of effectiveness and efficiency. In this paper, we present a flexible cluster-and-search approach that is able to reuse any previously proposed image descriptor as long as a suitable similarity function is provided. In the clustering step, the image data set is clustered using a hybrid divisive-agglomerative hierarchical clustering technique. The obtained clusters are organized in a tree that can be traversed efficiently using the similarity function associated with the chosen image descriptors. Our experiments have shown that we can improve search-time performance by a factor of 10 or more, at the cost of small loss in effectiveness (typically less than 15%) when compared to the state-of-the-art solutions.