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Application of genetic algorithm for image searching on example of STA

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Image tracking and recognition is one of the key data processing mechanism for humans. Because of that the implementation of image recognition mechanisms is visible in many branches of science. Mathematical approach to the problem allows to propose many different solutions, which effectiveness depends on the way of defining the problem. The following article shows one of the possibilities of applying genetic algorithm to solve the problem of image tracking from defined set of digital images. The aim of the article was to analyse the effectiveness of used genetic algorithm for image tracking. The article states a thesis, that it is possible to use a genetic algorithm utilizing Query By Image Content mechanism with an acceptable (by W3C standards) return time. Designed by author, variation of genetic algorithm working on defined set of digital images, was put under evaluation. Based on performed research, the effectiveness and speed of image tracking was evaluated with use of correlation coefficient and measures of algorithms time of work. Performed research showed the possibility of implementing the genetic algorithm in similar image tracking, with results exceeding the expectations of computer users.
Słowa kluczowe
Rocznik
Strony
9--16
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
  • Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland
Bibliografia
  • [1] Goldberg, D. E.: Klasyka Informatyki: Algorytmy genetyczne i ich zastosowania. Warsaw, Wydawnictwo Naukowo-Techniczne, 2003.
  • [2] Rutkowski, L.: Metody i techniki sztucznej inteligencji.Warsaw,Wydawnictwo Naukowe PWN, 2006.
  • [3] Mokrzycki, W.: Encyklopedia przetwarzania obrazów. Warsaw, Akademicka Oficyna Wydawnicza RM, 1992.
  • [4] Kopel, M.: Metody analizy spójności i zgodności kolekcji dokumentów WWW. Wrocaw, Wroclaw University of Technology, 2009.
  • [5] Lamport, L.: Web Content Accessibility Guidelines (WCAG) 2.0. WorldWideWeb Consortium, http://www.w3.org/TR/WCAG20/ (accessed: September 2013).
  • [6] Google Images. https://images.google.com/ (accessed: May 2015).
  • [7] Yahoo Image Search. https://images.search.yahoo.com/ (accessed: May 2015).
  • [8] MSN/Windows Live. http://www.msn.com/ (accessed: May 2015).
  • [9] Exalead. https://www.exalead.com/search/ (accessed: May 2015).
  • [10] Picsearch. http://www.picsearch.com/ (accessed: May 2015).
  • [11] Behold. http://www.behold.cc/ (accessed: May 2015).
  • [12] Like.com. http://www.like.com/ (accessed: May 2015).
  • [13] Xcavator.net. http://www.xcavator.net/ (accessed: May 2011).
  • [14] Ide Visual Search Lab. http:// labs.tineye.com/ (accessed: May 2015).
  • [15] Ide TinEye. http:// ideeinc.com/ (accessed: May 2015).
  • [16] Imense Picture Search. http://imense.com/ (accessed: May 2015).
  • [17] Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Qian Huang, Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: the QBIC system. Computer, Vol. 28, No. 9, pp. 23–32, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-45ce2e65-72b3-4087-aa8f-315486c506ea
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