A Compact Sift-Based Strategy for Visual Information Retrieval in Large Image Databases
DOI:
https://doi.org/10.26415/2572-004X-vol3iss2p402-412Keywords:
Scale Invariant Feature Transform, texture description, computer vision, image databases, iridology, content-based image retrieval, CBIR.Abstract
This paper applies the Standard Scale Invariant Feature Transform (S-SIFT) algorithm to accomplish the image descriptors of an eye region for a set of human eyes images from the UBIRIS database despite photometric transformations. The core assumption is that textured regions are locally planar and stationary. A descriptor with this type of invariance is sufficient to discern and describe a textured area regardless of the viewpoint and lighting in a perspective image, and it permits the identification of similar types of texture in a figure, such as an iris texture on an eye. It also enables to establish the correspondence between texture regions from distinct images acquired from different viewpoints (as, for example, two views of the front of a house), scales and/or subjected to linear transformations such as translation. Experiments have confirmed that the S-SIFT algorithm is a potent tool for a variety of problems in image identification.
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References
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[2] Lindeberg, T. Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics, 21(2):224-270, 1994. https://doi.org/10.1007/978-1-4757-6465-9
[3] Lowe, D. G. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision, Corfu, Greece, pp. 1150-1157,1999. https://doi.org/10.1109/ICCV.1999.790410
[4] Lowe, D. G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60, 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
[5]Cruz, B. F. Aplicacao da Tecnica SIFT na Identificacao de Olhos Humanos, M. Sc. Dissertation, Universidade Estadual do Rio de Janeiro, Nova Friburgo, RJ, Brazil, 2008.
[6] Proenca H. & Alexandre, L. A. UBIRIS: A noisy iris image database, Springer Lecture Notes in Computer Science - ICIAP 2005: 13th International Conf. on Im. Anal. and Proc., pages 970-977, 2005. https://doi.org/10.1007/11553595_119
[7] Aigrain, P. et al., Content-based representation and retrieval of visual media: A state-of-the-art review. Multimedia Tools and Applications 3(3), 179-202, 1996. https://doi.org/10.1007/BF00393937
[8] E. Chang, et all, RIME: A replicated image detector for the WWW, in Multimedia Storage and Archiving Systems III, (Kuo, C. et all, eds.), Proc SPIE 3527, 58-67, 1998. https://doi.org/10.1117/12.325852
[9] Cleverdon, C. and et al., Factors determining the performance of indexing systems, Cranfield College of Aeronautics,1966.
[10]Fidal, R. The image retrieval task: Implications for the design and evaluation of image databases. The New Review of Hypermedia and Multimedia, 3, 181-199, 1997. https://doi.org/10.1080/13614569708914689
[11] Flickner, M. et all, Query by image and video content: the QBIC system. IEEE Computer 28(9), 23-32, 1995. https://doi.org/10.1109/2.410146
[12] Forsythe, D. et al. Finding pictures of objects in large collections of images, Digital Image Access and Retrieval: 1996 Clinic on Library Applications of Data Processing (Heidorn, P. and Sandore, B. eds.), 118-139, 1997.
[13] Gupta, A. et al., The Virage image search engine: an open framework for image management, Proc. of Storage and Retrieval for Image and Video Databases IV, Proc SPIE 2670, pp 76-87, 1996.
[14] Jorgensen et al., Considerations in prototyping an image retrieval testbed. Proc. of the Multimedia Indexing and Retreival Workshop with the 22nd Annual International ACM SIGIR Conference, Berkeley, CA, August 19, 1999.
[15] Smith, J. & Chang, S. An image and video search engine for the World Wide Web, in Storage and Retrieval for Image and Video Databases V (Sethi, I. And Jain, R. eds.), Proc SPIE 3022, 84-95, 2007.
[16] Tuytelaars, T. & Mikolajczyk, K. A Survey on Local Invariant Features, 2006.
[17] Zheng, M. Metadata elements for object description and representation: A case report from a digitized historical fashion collection project, Journal of the Am. Soc. for Inf. Science, 50(13), pp. 1193-1208, 1999.
https://doi.org/10.1002/(SICI)1097-4571(1999)50:13<1193::AID-ASI5>3.0.CO;2-C
[18] Schaffalitzky, F. & A. Zisserman, A. Multi-view matching for unordered image sets, or How do I organize my holiday snaps?, Proc. European Conf. on Computer Vision, vol. 1, pp. 414-431, 2002. https://doi.org/10.1007/3-540-47969-4_28
[19] Mikolajczyk, K. & Schmid, C. A Performance Evaluation of Local Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615--1630, 2005. https://doi.org/10.1109/TPAMI.2005.188
PMid:16237996
[20] [20] Randen, T. and Husoy, J.H. Filtering for texture classification: a comparative study, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, no. 4, pp. 291-310, 1999. https://doi.org/10.1109/34.761261
[21] Todd, J.W., Whitson, H.E., & Marshall, E.C. Eye and Vision Health for Tomorrow: From Recommendations to Coordinated Action. JAMA ophthalmology, 2018. https://doi.org/10.1001/jamaophthalmol.2018.5923
PMid:30520949
[22] Daugman, J. "How iris recognition works," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 21-30, Jan 2004. https://doi.org/10.1109/TCSVT.2003.818350
[23] Jain,S. D. & Niranjan, U. C. Distributed framework for remote clinical diagnosis with visual query support, Proc. 5th IEEE International Conference on Information Technology and Application in Biomedicine, China, 2008. https://doi.org/10.1109/ITAB.2008.4570562
[24] van der Zwan, R. et al. The Informedia digital video library system at the Open University, Proc. CIR-99: the Challenge of Image Retrieval, Newcastle upon Tyne, 1999.
[25] Smith J. R. & Chang S. F. An image and video search engine for the World-Wide Web, Storage and Retrieval for Image and Video Databases V (Sethi, I K and Jain, R C (Eds), Proc SPIE 3022, 84-95, 1997.
https://doi.org/10.1117/12.263446
[26] Eakins J. P. Design criteria for a shape retrieval system, Computers in Industry 21, 167-184, 1993.
https://doi.org/10.1016/0166-3615(93)90134-M
[27] Eakins, J. P. Automatic image content retrieval - are we getting anywhere?, Proc. Third International Conference on Electronic Library and Visual Information Research (ELVIRA3), De Montfort University, Milton Keynes, pp. 123-135, 1996.
[28] Hebbar, H., Niranjan, U.,C., & Mushigeri, S. Content based Image Retrieval based on Cumulative Distribution Function: A Performance Evaluation. International Journal of Computer Applications 81(18):16-22, 2013. Published by Foundation of Computer Science, New York, USA. https://doi.org/10.5120/14223-2369
[29] Megalooikonomou, V. & Kontos, D. Integrating clinical information repositories: A framework for distributed analysis of medical image data, Proc. 5th International Network Conference (INC 2005), Special Session on Image, Signal and Distributed Data Processing for Networked eHealth Applications, Samos Island, Greece, July 2005, pp. 545-552.
[30] Yang, C.C. Content Based Image Retrieval: A Comparison between Query by Example and Image Browsing Map Approaches, Journal of Information Science, vol.30, no.3, 2004, pp.257-270. https://doi.org/10.1177/0165551504044670
[31] http://iris.di.ubi.pt (last time visited: 06-30-2019)
[32] Herrmann, A. E. & Estrela, V. V. Content-Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare. In M. Cruz-Cunha, I. Miranda, R. Martinho, & R. Rijo (Eds.), Encyclopedia of E-Health and Telemedicine (pp. 495-520). IGI Global, 2016. doi: 10.4018/978-1-4666-9978-6.ch03
[33] de Jesus, M. A. & V. V. Estrela. An Introduction to Data Mining Applied to Health-Oriented Databases. Orient. J. Comp. Sci. and Technol;9(3), 2016. https://doi.org/10.13005/ojcst/09.03.03
[34] Kamel, H., Chahir Y. & Kholladi, M.-K. SIFT Detectors for Matching Aerial Images in Reduced Space, Proc. 4th International Conference on Computer Integrated Manufacturing CIP'2007.
[35] Memon, Q. On Assisted Living of Paralyzed Persons through Real-Time Eye Features Tracking and Classification Using Support Vector Machines. Medical Technologies Journal, Vol. 3, no. 1, Apr. 2019, pp. 316-33,
https://doi.org/10.26415/2572-004X-vol3iss1p316-333
[36] Rachida, Z., A. BELAID, and D. Ben Salem. A Segmentation Method of Skin MRI 3D High Resolution in Vivo. Medical Technologies Journal, Vol. 2, no. 3, Sept. 2018, pp. 255-61, doi:https://doi.org/10.26415/2572-004X-vol2iss3p255-261.
[37] Coelho, A.M., Assis, J.T., & Estrela, V.V. Error concealment by means of clustered blockwise PCA. 2009 Picture Coding Symposium, 1-4, 2009. https://doi.org/10.1109/PCS.2009.5167442
[38] Coelho, A.M., & Estrela, V.V. EM-Based Mixture Models Applied to Video Event Detection. In Prin
cipal Component Analysis - Engineering Applications, Parinya Sanguansat (Ed.), IntechOpen, 2012.
[39] Song-song, Z. Video Vehicle Tracking Based on Dimension Reduction of SIFT Features. Computer Knowledge and Technology, 2012.
[40] Zhu, G., Wang, Q.G., Yuan, Y., & Yan, P. SIFT on manifold: An intrinsic description. Neurocomputing, 113, 227-233, 2013. https://doi.org/10.1016/j.neucom.2013.01.020
[41] Rivera, L.A., Estrela, V.V., & Carvalho, P.C. Oriented bounding boxes using multiresolution contours for fast interference detection of arbitrary geometry objects. Proc. WSCG 2004, 2004.
Published
2019-07-13
Issue
Section
Medical technologies
