Abstract
This paper presents a novel shape-based image retrieval model. We focused on the shape feature of objects inside images because there is evidence that natural objects are primarily recognized by their shapes. Using this feature of objects the semantic gap is reduced considerably. Our technique contains an alternative representation of shapes that we have called two segment turning function (2STF). Two segment turning function has a set of invariant features such as invariant to rotation, scaling and translation. Then, based on 2STF, we proposed a complete new strategy to compute a similarity among shapes. This new method was called Star Field (SF). To test the proposed technique, which is made up of a set of new methods mentioned above, a test-bed CBIR system was implemented. The name of this CBIR System is IRONS. IRONS stands for ”Image Retrieval based ON Shape”. Finally, we compared our results with a set of well known methods obtained similar results without the exhaustive search of many of them. This former feature of our proposal is one of the most important contributions of our technique to the visual information retrieval area.
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References
Blum, H.: Biological shape and visual science. Journal of Theor. Biol. 38, 205–287 (1973)
Chavez-Aragon, J.A.: Star Field Approach for Shape-Based Image Retrieval: Development, Analysis and Applications. Ph.d. Thesis, Universidad de las Americas-Puebla, Cholula Puebla Mexico (2007)
Chuang, G., Kuo, C.-C.: Wavelet descriptor of planar curves: Theory and applications. IEEE Trans. on Image Processing 5, 56–70 (1996)
Mokhtarian, S.A.F., Kittler, J.: Efficient and robust retrieval by shape content through curvature scale space. In: Smeulders, A.W.M., Jain, R. (eds.) Image Database and Multimedia Search, pp. 51–58. World Scientific Publising, Singapore (1997)
Biederman, I.: Recognition-by-components: a theory of human image undestanding. Psychological Review 94(2), 115–147 (1987)
Hirata, K., Kato, T.: Query by visual example - content-based image retrieval. In: EDBT 1992 Third International Conference on Extending Database Technology, vol. 1, pp. 56–71 (1992)
Siddiqi, S.J.D.K., Shokoufandeh, A., Zucker, S.W.: Shock graphs and shape matching. Int. J. of Computer Vision (2000)
Khotanzan, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Trans. PAMI 12, 489–497 (1990)
Latecki, L.J., Lakämper, R.: Contour-based shape similarity. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 441–454. Springer, Heidelberg (1999)
Latecki, L.J., Lakämper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Analysis and Machine Intelligence (2000)
Lin, I.-J, Kung, S.Y.: Coding and comparison of dags as a novel neural structure with application to on-line hadwritten recognition. IEEE Trans. Signal Processing (1996)
Longin, R.L., Latecki, J., Eckhardt.: Shape descriptors for non-ridig shape with a single closed contour. CVPR 2000 (2000)
Mokhtarian, F., Mackworth, A.K.: A theory of multiscale, curvature-based shape representation for planar curves. IEEE Trans. PAMI 14, 789–805 (1992)
Chan, Y., Kung, S.Y.: A hierarchical algorithm for image retrieval by sketch. First IEEE Workshop on Multimedia Signal Processing 1, 564–569 (1997)
Ye, J., Smeaton, A.F.: Aggregated Feature Retrieval for MPEG-7. In: Sebastiani, F. (ed.) ECIR 2003, vol. 2633 (2003)
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Chávez-Aragón, A., Starostenko, O. (2007). A Shape-Based Model for Visual Information Retrieval. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_58
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DOI: https://doi.org/10.1007/978-3-540-76631-5_58
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