PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Similarity detection of image using vector quantization and compression

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Detekcja podobieństwa obrazu bazująca na kwantyzacji wektora i kompresji
Języki publikacji
EN
Abstrakty
EN
In every day is a lot of new images and photos get into the internet. Problem of image similarity is up-to-date in image retrieval. There are a lot of methods for comparison of images. We use vector quantization and NCD method for look for similar images in collection that vector quantization prepares image files for NCD. In this paper we can show how to convert 2D image into 1D string using by vector quantization and how NCD method is used for image similarity detection.
PL
W artykule analizowano problem podobieństwa obrazu. Użyto metody kwantyzacji wektora i metody NCD. Pokazano jak konwertować obraz 2D w strumień 1D.
Rocznik
Strony
62--64
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
  • VSB – Technical University of Ostrava
autor
  • VSB – Technical University of Ostrava
autor
  • VSB – Technical University of Ostrava
autor
  • VSB – Technical University of Ostrava
Bibliografia
  • [1] Yong Rui, Thomas S. Huang, Image Retrieval: Current Techniques, Promising Directions And Open Issues, Journal of Visual Communication and Image Representation (1999), No. 5, pages 39-62
  • [2] C. S. McCamy, H. Marcus, and J. G. Davidson, A color-rendition chart, Journal of Applied Photographic Engineering 2(3), 1976.
  • [3] Jonathan Mortensen, Jia Jie Wu, Jacob Furst, John Rogers, Effect of Image Linearization on Normalized Compression Distance
  • [4] M. Swain and D. Ballard, Color indexing, International Journal of Computer Vision 7(1), 1991.
  • [5] M. Ioka, A Method of Defining the Similarity of Images on the Basis of Color Information, Technical Report RT-0030, IBM Research, Tokyo Research Laboratory, Nov. 1989.
  • [6] W. Niblack, R. Barber, and et al., The QBIC project: Querying images by content using color, texture and shape, in Proc SPIE Storage and Retrieval for Image and Video Databases, Feb. 1994.
  • [7] M. Stricker and M. Orengo, Similarity of color images, in Proc. SPIE Storage and Retrieval for Image and Video Databases, 1995.
  • [8] R. M. Haralick, K. Shanmugam, and I. Dinstein, Texture features for image classification, IEEE Trans. On Sys. Man. and Cyb. SMC-3(6), 1973.
  • [9] H. Tamura, S. Mori, and T. Yamawaki, Texture features corresponding to visual perception, IEEE Trans. On Sys., Man. and Cyb. SMC-8(6), 1978.
  • [10] A. Granados. Analysis and study on text representation to improve the accuracy of the normalized compression distance. AI Commun., 25(4):381-384, 2012.
  • [11] S. Dubnov, G. Assayag, O. Lartillot, and G. Bejerano. Using machine-learning methods for musical style modeling. Computer, 36(10):73-80, 2003. cited By (since 1996)25.
  • [12] M. Li, J. Badger, X. Chen, S. Kwong, P. Kearney, and H. Zhang. An informationbased sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics, 17(2):149-154, 2001. cited By (since 1996)274.
  • [13] D. Benedetto, E. Caglioti, and V. Loreto. Language trees and zipping. Physical Review Letters, 88(4):487021-487024, 2002. cited By (since 1996)145.
  • [14] M. Li, X. Chen, X. Li, B. Ma, and P. M. B. Vitanyi. The similarity metric. IEEE Transactions on Information Theory, 50(12):3250{-264, 2004.
  • [15] J. J. Vayrynen, T. Tapiovaara, K. Kettunen, and M. Dobrinkat. Normalized compression distance as an automatic MT evaluation metric. In Proceedings of MT 25years on, 21-22 Nov 2009 Craneld, UK, to appear
  • [17] D. Sculley and C. Brodley. Compression and machine learning: A new perspectiveon feature space vectors. pages 332-341, 2006. cited By (since 1996)17.
  • [18] P. M. B. Vit nyi. Universal similarity. CoRR, abs/cs/0504089, 2005.
  • [19] R. Cilibrasi and P. M. B. Vitanyi. Clustering by compression. IEEE Transactionson Information Theory, 51(4):1523-1545, 2005.
  • [20] J. Walder, M. Kratky, R. Baca, J. Platos, and V. Snasel. Fast decoding algorithms for variable-lengths codes. Inf. Sci.,183(1):66-91, 2012.
  • [21] A. Gersho and R. M. Gray. Vector quantization and signal compression. Kluwer Academic Publishers, Norwell, MA, USA,
  • [22] J. Angulo. Computational color imaging. chapter Structure Tensor of Colour Quaternion Image Representations for Invariant Feature Extraction, pages 91-100. Springer-Verlag, Berlin, Heidelberg, 2009.
  • [23] C.-Y. C. Tzu-Chuen Lu. A Survey of VQ Codebook Generation. Journal of Information Hiding and Multimedia Signal Processing, 2010.
  • [24] http://tabby.vision.mcgill.ca/html/welcome.html. [Online; accessed 11.4.2013]
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0aa58aed-a988-429b-ae54-99d7a9c9b5bf
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.