Abstract
In the world of computer imaging, we still do not have a good and fast enough method for image searching. This is because science is still not able to imitate fully functions of the human brain. When humans think about images, they do not think about mathematical formulas, matrices, histograms etc. Those mathematical and algorithmic methods are very good for e.g. computer face detection or number plate recognition, but we cannot directly use them for analyzing a whole image and for searching in a set of thousands or even millions of images. On the other hand, computers are able to scan millions of documents, searching for some phrase or even a single word. Fast text search is fully supported by a majority of significant database systems such as Oracle, PostgreSQL or MS SQL Server. The paper presents fast text search engine from another point of view, that is, its application in content based image retrieval.
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References
Ali, M., Clausi, D.: Using the Canny edge detector for feature extraction and enhancement of remote sensing images. IGARSS 2001 Scanning the Present and Resolving the Future Proceedings IEEE 2001 International Geoscience and Remote Sensing Symposium Cat No01CH37217, pp. 2298–2300 (2001)
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)
Brown, M., Lowe, D.: Recognising Panoramas. In: Proceedings of the 9th International Conference on Computer Vision (ICCV 2003), Nice, France, pp. 1218–1225 (2003)
Brown, M., Lowe, D.: Automatic Panoramic Image Stitching using Invariant Features. International Journal of Computer Vision 74(1), 59–73 (2007)
Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Analysis: Theory, Methods & Applications 71(12), 1659–1672 (2009)
Derpanis, K.G.: The Harris Corner Detector (2004), www.cse.yorku.ca/~kosta/CompVis_Notes/harris_detector.pdf
Ghahroudi, M.R., Sarshar, M.R., Sabzevari, R.: A Novel Content-Base Image Retrieval Techniques Using Tree matching. In: Proceedings of the World Congress on Engineering 2008, London, U.K., vol. III (2008)
Grąbczewski, K., Jankowski, N.: Saving time and memory in computational intelligence system with machine unification and task spooling. Knowledge-Based Systems 24(5), 570–588 (2011)
Jankowski, N., Grąbczewski, K.: Universal Meta-Learning Architecture and Algorithms. In: Jankowski, N., Duch, W., Grąbczewski, K. (eds.) Meta-Learning in Computational Intelligence. SCI, vol. 358, pp. 1–76. Springer, Heidelberg (2011)
Kazakova, N., Margala, M., Durdle, N.G.: Sobel edge detection processor for a real-time volume rendering system. In: Proceedings of the 2004 International Symposium on Circuits and Systems, ISCAS 2004, vol. 2, II-913–II-916 (2004)
Mikołajczyk, K., Schmid, C.: A Performance of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Inteligence 27(10), 1615–1630 (2005)
Nowicki, R., Rutkowski, L.: Soft Techniques for Bayesian Classification. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. AISC, pp. 537–544. Springer Physica-Verlag (2003)
PostgreSQL 9.1.2 Documentation, http://www.postgresql.org/docs/9.1/static/textsearch.html
Rutkowski, L., Cierniak, R.: On image compression by competitive neural networks and optimal linear predictors. Signal Processing: Image Communication - a Eurosip Journal 15(6), 559–565 (2000)
Valgren, C., Lilienthal, A.: SIFT, SURF and Seasons: Long-term Outdoor Localization Using Local Features. In: Proc. European Conference on Mobile Robots, pp. 253–258 (2007)
Yan, K., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II-506–II-513 (2004)
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Rygał, J., Najgebauer, P., Nowak, T., Romanowski, J., Gabryel, M., Scherer, R. (2012). Properties and Structure of Fast Text Search Engine in Context of Semantic Image Analysis. 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_69
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DOI: https://doi.org/10.1007/978-3-642-29347-4_69
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