Efficient Content-Based Image Retrieval through Metric Histograms

Authors:
A. J. M. Traina;C. Traina;J. M. Bueno;F. J. T. Chino;P. Azevedo-Marques
Affiliations:
Computer Science Department, University of Sao Paulo at Sao Carlos, Brazil agma@icmc.usp.br;Computer Science Department, University of Sao Paulo at Sao Carlos, Brazil caetano@icmc.usp.br;Computer Science Department, University of Sao Paulo at Sao Carlos, Brazil josiane@icmc.usp.br;Computer Science Department, University of Sao Paulo at Sao Carlos, Brazil chino@icmc.usp.br;Science of Image and Medical Physics Center, Medical School of Ribeirao Preto, University of Sao Paulo at Ribeirao Preto, Brazil pmarques@fmrp.usp.br
Venue:
World Wide Web
Year:
2003

Citing 0
Cited 5

A new similarity measure for histograms applied to content-based retrieval of medical images

Proceedings of the 2006 ACM symposium on Applied computing
Efficient processing of complex similarity queries in RDBMS through query rewriting

CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A system design for content-based image retrieval and analysis of mammograms using PostgreSQL with image-handling extension

BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Integrating images to patient electronic medical records through content-based retrieval techniques

CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Nested Partitions Properties for Spatial Content Image Retrieval

International Journal of Digital Library Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique works on each image independently from the others in the dataset, therefore there is no pre-defined number of color regions in the resulting histogram. Thus, it is not possible to use traditional comparison algorithms such as Euclidean or Manhattan distances. To allow the comparison of images through the new feature vectors given by metric histograms, a new metric distance function MHD( ) is also proposed. This paper shows the improvements in timing and retrieval discrimination obtained using metric histograms over traditional ones, even when using images with different spatial resolution or thumbnails. The experimental evaluation of the new method, for answering similarity queries over two representative image databases, shows that the metric histograms surpass the retrieval ability of traditional histograms because they are invariant on geometrical and brightness image transformations, and answer the queries up to 10 times faster than the traditional ones.