Image Indexing Techniques
Chapter
First Online:
Abstract
Images are described by various forms of feature descriptors. Especially local invariant features have gained a wide popularity Lowe (Int J Comput Vis 60:91–110, 2004 [17]), Matas et al. (Image Vis Comput 22:761–767, 2004 [18]), Mikolajczyk et al. (Int J Comput Vis 60:63–86, 2004 [20]), Nister Stewenius (Scalable recognition with a vocabulary tree, pp. 2161–2168, 2006 [25]) and Sivic and Zisserman (Video google: a text retrieval approach to object matching in videos, pp. 1470–1477, 2003 [35]).
References
- 1.Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
- 2.Bayer, R., McCreight, E.M.: Organization and maintenance of large ordered indexes. Acta Informatica 1(3), 173–189 (1972). https://doi.org/10.1007/BF00288683CrossRefGoogle Scholar
- 3.Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM (2007)Google Scholar
- 4.Bradski, G.: The opencv library. Dr. Dobbs J. 25(11), 120–126 (2000)Google Scholar
- 5.Buckland, M., Gey, F.: The relationship between recall and precision. J. Am. Soc. Inf. Sci. 45(1), 12 (1994)CrossRefGoogle Scholar
- 6.Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, SCG 2004, pp. 253–262. ACM, New York, NY, USA (2004)Google Scholar
- 7.Edelkamp, S., Schroedl, S.: Heuristic Search: Theory and Applications. Elsevier (2011)Google Scholar
- 8.Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
- 9.Grauman, K., Darrell, T.: Efficient image matching with distributions of local invariant features. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, pp. 627–634 vol. 2 (2005). https://doi.org/10.1109/CVPR.2005.138
- 10.Grycuk, R., Gabryel, M., Scherer, M., Voloshynovskiy, S.: Image descriptor based on edge detection and crawler algorithm. In: International Conference on Artificial Intelligence and Soft Computing, pp. 647–659. Springer International Publishing (2016)Google Scholar
- 11.Grycuk, R., Gabryel, M., Scherer, R., Voloshynovskiy, S.: Multi-layer architecture for storing visual data based on WCF and microsoft sql server database. In: Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, vol. 9119, pp. 715–726. Springer International Publishing (2015)Google Scholar
- 12.Grycuk, R., Gabryel, M., Scherer, R., Voloshynovskiy, S.: Multi-layer architecture for storing visual data based on WCF and microsoft sql server database. In: International Conference on Artificial Intelligence and Soft Computing, pp. 715–726. Springer International Publishing (2015)Google Scholar
- 13.Hamzah, R.A., Rahim, R.A., Noh, Z.M.: Sum of absolute differences algorithm in stereo correspondence problem for stereo matching in computer vision application. In: 2010 3rd International Conference on Computer Science and Information Technology, vol. 1, pp. 652–657 (2010). https://doi.org/10.1109/ICCSIT.2010.5565062
- 14.Korytkowski, M.: Novel visual information indexing in relational databases. Integr. Comput. Aided Eng. 24(2), 119–128 (2017)CrossRefGoogle Scholar
- 15.Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Information Sciences 327, 175–182 (2016). https://doi.org/10.1016/j.ins.2015.08.030. URL http://www.sciencedirect.com/science/article/pii/S0020025515006180MathSciNetCrossRefGoogle Scholar
- 16.Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE (2006)Google Scholar
- 17.Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
- 18.Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004). British Machine Vision Computing 2002CrossRefGoogle Scholar
- 19.Meskaldji, K., Boucherkha, S., Chikhi, S.: Color quantization and its impact on color histogram based image retrieval accuracy. In: Networked Digital Technologies, 2009. NDT 2009. First International Conference on, pp. 515–517 (2009). https://doi.org/10.1109/NDT.2009.5272135
- 20.Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)CrossRefGoogle Scholar
- 21.Najgebauer, P., Grycuk, R., Scherer, R.: Fast two-level image indexing based on local interest points. In: 2018 23rd International Conference on Methods Models in Automation Robotics (MMAR), pp. 613–617 (2018). https://doi.org/10.1109/MMAR.2018.8485831
- 22.Najgebauer, P., Korytkowski, M., Barranco, C.D., Scherer, R.: Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12–16, 2016, Proceedings, Part II, chap. Novel Image Descriptor Based on Color Spatial Distribution, pp. 712–722. Springer International Publishing, Cham (2016)Google Scholar
- 23.Najgebauer, P., Nowak, T., Romanowski, J., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image retrieval by dictionary of local feature descriptors. In: 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, July 6–11, 2014, pp. 512–517 (2014)Google Scholar
- 24.Najgebauer, P., Rygal, J., Nowak, T., Romanowski, J., Rutkowski, L., Voloshynovskiy, S., Scherer, R.: Fast dictionary matching for content-based image retrieval. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, vol. 9119, pp. 747–756. Springer International Publishing (2015)Google Scholar
- 25.Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, CVPR 2006, pp. 2161–2168. IEEE Computer Society, Washington, DC, USA (2006)Google Scholar
- 26.Nowak, T., Najgebauer, P., Romanowski, J., Gabryel, M., Korytkowski, M., Scherer, R., Kostadinov, D.: Spatial keypoint representation for visual object retrieval. In: Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, vol. 8468, pp. 639–650. Springer International Publishing (2014)Google Scholar
- 27.Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Computer Vision and Pattern Recognition, 2007. CVPR 2007. IEEE Conference on, pp. 1–8 (2007)Google Scholar
- 28.Richardson, I.E.: H. 264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. Wiley (2004)Google Scholar
- 29.Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011). https://doi.org/10.1109/ICCV.2011.6126544
- 30.Rutkowski, L.: Flexible Neuro-Fuzzy Systems. Kluwer Academic Publishers (2004)Google Scholar
- 31.Rutkowski, L.: Computational Intelligence Methods and Techniques. Springer, Berlin, Heidelberg (2008)CrossRefGoogle Scholar
- 32.Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI 1999, pp. 1401–1406. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)Google Scholar
- 33.Scherer, R.: Designing boosting ensemble of relational fuzzy systems. Int. J. Neural Syst. 20(5), 381388 (2010). http://www.worldscinet.com/ijns/20/2005/S0129065710002528.htmlCrossRefGoogle Scholar
- 34.Scherer, R.: Multiple Fuzzy Classification Systems. Springer Publishing Company, Incorporated (2014)zbMATHGoogle Scholar
- 35.Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003. vol. 2, pp. 1470–1477 (2003)Google Scholar
- 36.Sopyla, K., Drozda, P., Górecki, P.: Svm with cuda accelerated kernels for big sparse problems. In: ICAISC (1), Lecture Notes in Computer Science, vol. 7267, pp. 439–447. Springer (2012)Google Scholar
- 37.Tao, D.: The corel database for content based image retrieval (2009)Google Scholar
- 38.Tao, D., Li, X., Maybank, S.J.: Negative samples analysis in relevance feedback. IEEE Trans. Knowl. Data Eng. 19(4), 568–580 (2007)CrossRefGoogle Scholar
- 39.Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1088–1099 (2006)CrossRefGoogle Scholar
- 40.Tieu, K., Viola, P.: Boosting image retrieval. Int. J. Comput. Vis. 56(1–2), 17–36 (2004)CrossRefGoogle Scholar
- 41.Ting, K.M.: Precision and recall. In: Encyclopedia of Machine Learning, pp. 781–781. Springer (2011)Google Scholar
- 42.Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol. 1, pp. I–511–I–518 (2001)Google Scholar
- 43.Voloshynovskiy, S., Diephuis, M., Kostadinov, D., Farhadzadeh, F., Holotyak, T.: On accuracy, robustness, and security of bag-of-word search systems. In: IS&T/SPIE Electronic Imaging, pp. 902, 807–902,807. International Society for Optics and Photonics (2014)Google Scholar
- 44.Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 1794–1801. IEEE (2009)Google Scholar
- 45.Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. In: Conference on Computer Vision and Pattern Recognition Workshop, 2006. CVPRW 2006, pp. 13–13 (2006). https://doi.org/10.1109/CVPRW.2006.121
- 46.Zhang, W., Yu, B., Zelinsky, G., Samaras, D.: Object class recognition using multiple layer boosting with heterogeneous features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 2, pp. 323–330 vol. 2 (2005). https://doi.org/10.1109/CVPR.2005.251
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