Abstract
Recently, the field of CBIR has attracted a lot of attention in the literature. In this paper, the problem of visually similar image retrieval has been investigated. For this task we use the methods derived from the Bag of Visual Words approach, such as Scale Invariant Feature Transform (SIFT) for identifying image keypoints and K-means to build a visual dictionary. To create a ranking of similar images, a novel Ranking by K-means Voting algorithm is proposed. The experimental section shows that our method works well for similar image retrieval. It turned out that our results are more accurate in comparison with a classical similarity measure based on the Euclidean metric in the order of 6% - 15%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: Current status and future directions. J. Digital Imaging 24(2), 208–222 (2011)
Aschkenasy, S.V., Jansen, C., Osterwalder, R., Linka, A., Unser, M., Marsch, S., Hunziker, P.: Unsupervised image classification of medical ultrasound data by multiresolution elastic registration. Ultrasound Med. Biol. 32(7), 1047–1054 (2006)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)
Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing 14(12), 2091–2106 (2005)
Ee, P., Report, P.: Histogram-based color image retrieval. Image Rochester NY, pp. 1–21 (2008)
Górecki, P., Artiemjew, P., Drozda, P., Sopyła, K.: Categorization of Similar Objects using Bag of Visual Words and Support Vector Machines. Accepted for Fourth International Conference on Agents and Artificial Intelligence. IEEE (2012)
Han, J., Ma, K.K.: Fuzzy color histogram and its use in color image retrieval. IEEE Transactions on Image Processing 11(8), 944–952 (2002)
He, C., Zheng, Y.F., Ahalt, S.C.: Object tracking using the gabor wavelet transform and the golden section algorithm. IEEE Trans. Multimedia 4, 528–538 (2002)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 1997), pp. 762–768. IEEE Computer Society, Washington, DC (1997)
Joachims, T.: Optimizing search engines using clickthrough data. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 133–142 (2002)
Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–513 (2004)
Keysers, D., Dahmen, J., Ney, H., Wein, B.B., Lehmann, T.M.: Statistical framework for model-based image retrieval in medical applications. Journal of Electronic Imaging 12(1), 59–68 (2003)
Korytkowski, M., Scherer, R., Rutkowski, L.: On combining backpropagation with boosting. In: 2006 International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, pp. 1274–1277 (2006)
Lam, M.O., Disney, T., Raicu, D.S., Furst, J., Channin, D.S.: Briscan open source pulmonary nodule image retrieval framework. Journal of Digital Imaging 20(suppl. 1), 63–71 (2007)
Lee, Y., Lee, K., Pan, S.: Local and Global Feature Extraction for Face Recognition. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 219–228. Springer, Heidelberg (2005)
Li, J.: The application of cbir-based system for the product in electronic retailing. In: 2010 IEEE 11th International Conference on Computer-Aided Industrial Design and Conceptual Design (CAIDCD), pp. 1327–1330 (November 2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Mikolajczyk, K., Leibe, B., Schiele, B.: Local Features for Object Class Recognition. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, pp. 1792–1799. IEEE (2005)
Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 530–535 (1997)
Simoncelli, E.P., Freeman, W.T.: The steerable pyramid: A flexible architecture for multi-scale derivative computation, pp. 444–447 (1995)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis. 3, 177–280 (2008)
Vo, A.P.N., Nguyen, T.T., Oraintara, S.: Texture image retrieval using complex directional filter bank. In: ISCAS (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Górecki, P., Sopyła, K., Drozda, P. (2012). Ranking by K-Means Voting Algorithm for Similar Image Retrieval. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-29347-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29346-7
Online ISBN: 978-3-642-29347-4
eBook Packages: Computer ScienceComputer Science (R0)