Quantum Inspired Shape Representation for Content Based Image Retrieval

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DOI: 10.4236/jsip.2014.52008    2,852 Downloads   4,201 Views   Citations

ABSTRACT

Content Based Image Retrieval (CBIR) is a technique in which images are indexed based on their visual contents and retrieving is only based upon these indexed images contents. Among the visual contents to describe the image details is shape. Shape of object, is considered as the most important distinguishable feature which living things can easily recognize, which is also a fact while this line is being written, and large efforts are currently underway in describing image contents by their shapes. Inspired by the core foundation of quantum mechanics, a new easy shape representation for content based image retrieval is proposed by borrowing the concept of quantum superposition into the basis of distance histogram. Results show better retrieval accuracy of the proposed method when compared with distance histogram.

Cite this paper

Jobay, R. and Sleit, A. (2014) Quantum Inspired Shape Representation for Content Based Image Retrieval. Journal of Signal and Information Processing, 5, 54-62. doi: 10.4236/jsip.2014.52008.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Rui, Y., Huang, T.S. and Chang, S.F. (1997) Image Retrieval: Past, Present and Future. In: Liao, M., Ed., Proceedings of the International Symposiumon Multimedia Information Processing, Taipei, 11-13 December 1997.
[2] del Bimbo, A. (1999) Visual Information Retrieval. Academic Press, London.
[3] Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A. and Jain, R. (2000) Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1349-1380.
[4] Kuo, W.-J., Chang, R.-F., Lee, C.C., Moon, W.K. and Chen, D.-R. (2002) Retrieval Technique for the Diagnosis of Solid Breast Tumors on Sonogram. Ultrasound in Medicine and Biology, 28, 903-909.
http://dx.doi.org/10.1016/S0301-5629(02)00541-0.
[5] Antani, S., Long, L.R. and Thoma, G.R. (2004) Content-Based Image Retrieval for Large Biomedical Image Archives. In: Proceedings of 11th World Cong Medical Informatics, San Francisco, 7-11 September 2004, 829-833.
[6] Rauber, T.W. (1994) Two-Dimensional Shape Description. Technical Report: GRUNINOVA-RT-10-94, Universidade Nova de Lisboa, Lisoba, Portugal.
[7] Loncaric, S. (1998) A Survey of Shape Analysis Techniques. Pattern Recognition, 31, 983-1001.
http://dx.doi.org/10.1016/S0031-2023(97)00122-2
[8] Zhang, D. and Lu, G. (2004) Review of Shape Representation and Description Techniques. Pattern Recognition, 37, 1-19.
http://dx.doi.org/10.1016/j.patcog.2003.07.008.
[9] Safar, M., Shahabi, C. and Sun, X. (2000) Image Retrieval by Shape: A Comparative Study. In: Proceedings of IEEE International Conference on Multimedia and Expo, New York, 30 July-2 August 2000, 141-144.
[10] Zhang, G., Ma, Z.M., Tong, Q., He, Y. and Zhao, T.N. (2008) Shape Feature Extraction Using Fourier Descriptors with Brightness in Content-Based Medical Image Retrieval. International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Harbin, 15-17 August 2008, 71-74.
http://dx.doi.org/10.1109/IIH-MSP.2008.16
[11] Wu, Y.Y. and Wu, Y.Q. (2009) Shape-Based Image Retrieval Using Combining Global and Local Shape Features. CISP 2nd International Congress on Image and Signal Processing, Tianjin, 17-19 October 2009, 1-5.
[12] Sleit, A., Salah, I. and Jabay, R. (2008) Approximating Images Using Minimum Bounding Rectangles. In: The First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2008), Ostrava, 4-6 August 2008, 394-396.
[13] Sleit, A., Abu-Areda, A. and Al-Hasan, H. (2010) Shape Approximation Using Circular Grids. WSEAS Transactions on Information Science and Applications, 7, 542-551.
[14] Zhang, W., Dickinson, S., Sclaroff, S., Feldman, J. and Dunn, S. (1998) Shape-Based Indexing in a Medical Image Database. IEEE Workshop on Biomedical Image Analysis, Santa Barbara, 26-27 June 1998, 221-230.
[15] Sajjanhar, A. and Lu, G. (1997) A Grid Based Shape Indexing and Retrieval Method. The Australian Computer Journal, 29, 131-140.
[16] Sajjanhar, A. (2003) Spatial Information in Histograms for Shape Representation. In: Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science, Vol. 2690, Springer, Berlin, 855-859.
http://dx.doi.org/10.1007/978-3-540-45080-1_118
[17] Sajjanhar, A., Lu, G. and Zhang, D.S. (2004) Coherence Based Histograms for Shape Retrieval. Proceedings of International Conference on Computer Science, Software Engineering Information Technology, E-Business and Applications (CSITeA04), Cairo, 27-29 December 2004, 27-29.
[18] Fan, S. (2001) Shape Representation and Retrieval Using Distance Histograms. Technical Report, University of Alberta, Alberta.
[19] Super, B.J. (2004) Fast Correspondence-Based System for Shape Retrieval. Pattern Recognition Letters, 25, 217-225.
http://dx.doi.org/10.1016/j.patrec.2003.10.003
[20] Lo, H.K., Popescu, S. and Spiller, T. (1998) Introduction in Quantum Information and Computation. World Scientific, Singapore.
[21] Brooks, M. (1999) Quantum Computing and Communication. Springer Verlag, London.
http://dx.doi.org/10.1007/978-1-4471-0839-9
[22] Eldar, Y.C. and Oppenheim, A.V. (2002) Quantum Signal Processing. IEEE Signal Processing Magazine, 19, 12-32.
http://dx.doi.org/10.1109/MSP.2002.1043298
[23] Tseng, C.C. and Hwang, T.M. (2003) Quantum Digital Image Processing Algorithms. 16th IPPR Conference on Computer Vision, Graphics and Image Processing, Kinmen, 17-19 August 2003, 827-834.
[24] Noell, M., Omer, B. and Suda, M. (2007) Quantum Information Algorithms—New Solutions for Known Problems. e & i Elektrotechnik und Informationstechnik, 124, 154-157.
http://dx.doi.org/10.1007/s00502-007-0434-7
[25] Veltkamp, R.C. and Hagedoorn, M. (2000) State of the Art in Shape Matching. In: Lew, M.S., Ed., Principles of Visual Information Retrieval, Springer-Verlag, London, 87-119.

  
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