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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2010 - 8

 HIGHLY CITED PAPER 

From Content-Based Image Retrieval by Shape to Image Annotation

MOCANU, I. See more information about MOCANU, I. on SCOPUS See more information about MOCANU, I. on IEEExplore See more information about MOCANU, I. on Web of Science
 
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Download PDF pdficon (1,949 KB) | Citation | Downloads: 1,156 | Views: 3,920

Author keywords
image annotation, image representations, image retrieval by content, genetic algorithm, shape retrieval

References keywords
image(11), retrieval(10), information(6), systems(5), shape(5), multimedia(5), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-11-30
Volume 10, Issue 4, Year 2010, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.04008
Web of Science Accession Number: 000284782700008
SCOPUS ID: 78649708171

Abstract
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In many areas such as commerce, medical investigations, and others, large collections of digital images are being created. Search operations inside these collections of images are usually based on low-level features of objects contained in an image: color, shape, texture. Although such techniques of content-based image retrieval are useful, they are strongly limited by their inability to consider the meaning of images. Moreover, specifying a query in terms of low level features may not be very simple. Image annotation, in which images are associated with keywords describing their semantics, is a more effective way of image retrieval and queries can be naturally specified by the user. The paper presents a combined set of methods for image retrieval, in which both low level features and semantic properties are taken into account when retrieving images. First, it describes some methods for image representation and retrieval based on shape, and proposes a new such method, which overcomes some of the existing limitations. Then, it describes a new method for image semantic annotation based on a genetic algorithm, which is further improved from two points of view: the obtained solution value - using an anticipatory genetic algorithm, and the execution time - using a parallel genetic algorithm.


References | Cited By  «-- Click to see who has cited this paper

[1] T. Kato, "Database architecture for content-based image retrieval", Proc. SPIE 1662, vol. 112, pp. 112-123, Feb. 1992.

[2] L. R. B. Schomaker, E. de Leau and L. G. Vuurpijl, "Using pen-based outlines for object-based annotation and image-based queries", Visual Information and Information Systems, pp. 585-592, 1999.

[3] G. Lu. Multimedia Database Management Systems. Artech House Publishers, Boston, 1999.

[4] E. Persoon and K. S. Fu, "Shape discrimination using Fourier Descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, pp. 388-397, 1986.
[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 185]


[5] C. Zahn and R. Roskies, "Fourier descriptors for plane closed curves", IEEE Trans. On Computer, vol. 21, pp. 269-281, 1972.
[CrossRef] [SCOPUS Times Cited 1444]


[6] D. Zhang and G. Lu, "A comparative study on shape retrieval using fourier descriptors with different shape signatures", Proceedings of International Conference on Intelligent Multimedia and Distance Education, pp. 1-9, 2001.

[7] E. M. Arkin, L. P. Chew, D. P. Huttenlocher, K. Kedem and J. S. B. Mitchell, "An efficiently computable metric for comparing polygonal shapes", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 209-216, 1991.
[CrossRef] [Web of Science Times Cited 339] [SCOPUS Times Cited 477]


[8] K. L. Tan,, B. C. Ooi and L. F. Thiang, "Retrieving similar shapes efficiently", Multimedia Tools and Applications, vol. 19, no. 2, pp. 111-134, 2003.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 22]


[9] S. Fan, "Shape representation and retrieval using distance histograms", Technical Report of University of Alberta, 2001.

[10] I. Mocanu, "Image retrieval by shape based on contour techniques: a comparative study", Applied Computational Intelligence and Informatics, pp. 219-223, 2007.

[11] L. P. Cordella and G. Dettori, "An O(N) algorithm for polygonal approximation", Pattern Recognition Letters, vol. 3, pp. 93-97, 1985.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]


[12] F. Mokhtarian, J. Kittler and S. Abbasi, "The Squid project", in press.

[13] I. Mocanu, "Optimization algorithms in multimedia databases", PhD Thesis.

[14] J. P. Eakins, "Automatic image content retrieval - are we getting anywhere ?", Proceedings of Third International Conference on Electronic Library and Visual Information Research, pp. 123-135, 1996.

[15] J. P. Eakins, "Techniques for image retrieval", Library and Information Briefings, in press.

[16] V. N. Gudivada and V. V. Raghavan, "Content-based image retrieval systems", IEEE Computer, vol. 28, no. 9, pp. 18-22, 1995.

[17] Y. Mori, H. Takahashi, and R. Oka, "Image-to-word transformation based on dividing and vector quantizing images with words", Proceedings of the International Workshop on Multimedia Intelligent Storage and Retrieval Management, 1999.

[18] P. Duygulu, K. Barnard, J. F. G. de Freitas and D. A. Forsyth, "Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary", European Conference on Computer Vision, pp. 97-112, 2002.

[19] J. Jeon, V. Lavrenko and R. Manmatha, "Automatic image annotation and retrieval using cross-media relevance models", ACM SIGIR, pp. 119-126, 2003.

[20] P. Panagi, S. Dasiopoulou, G. Th. Papadopoulos, I. Kompatsiaris and M. G. Strintzis, "A genetic algorithm approach to ontology-driven demantic image analysis", IET International Conference on Visual Information Engineering, pp. 132-137, 2006.
[CrossRef]


[21] S. Jeong, "Histogram-based color image retrieval", Psych221/EE362 Project Report. Mar.15, 2001.

[22] M. J. Swain and D. H. Ballard, D. H., "Color indexing", International Journal of Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[CrossRef] [Web of Science Times Cited 3322] [SCOPUS Times Cited 4337]


[23] J. B. MacQueen, "Some methods for classification and analysis of multivariate observations", Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp 281-297, 1967.

[24] I. Mocanu, E. Kalisz and L. Negreanu, "Genetic algorithms viewed as anticipatory systems", AIP Conference Proceedings, to be published.

[25] M. Nowostawski and R. Poli, "Parallel genetic algorithm taxonomy", Knowledge-Based Intelligent Information Engineering Systems, pp. 88-92, 1999.



References Weight

Web of Science® Citations for all references: 3,825 TCR
SCOPUS® Citations for all references: 6,482 TCR

Web of Science® Average Citations per reference: 147 ACR
SCOPUS® Average Citations per reference: 249 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-10-18 04:26 in 50 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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