0452147 20240103211405.420151208235959.9 000366844400011 84949479880 10.1016/j.patrec.2015.10.012 10 s. P cav_un_epca*02573890167-8655691 (2016)72-81Elsevier Image retrieval Noisy image Histogram Convolution Moments Invariants cav_un_auth*0282545 Höschl Cyril Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101087 Flusser Jan Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/ZOI/hoschl-0452147.pdf GA15-16928S GA ČR cav_un_auth*0314467 We present a histogram-based image retrieval method which is designed specifically for noisy query images. The images are reretrieved according to histogram similarity. To reach high robustness to noise, the histograms are described by newly proposed features which are insensitive to a Gaussian additive noise in the original images. The advantage of the new method is proved theoretically and demonstrated experimentally on real data. 2016 JD 2 4 A hod 4ah 20231122141346.5 RVO:67985556 http://hdl.handle.net/11104/0253176 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS S 2 Article Computer Science Artificial Intelligence COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 2.164 0.334 9.1 0.01282 0.662 9609 0.729 Q1 1.861 287 Q2 56.615 56.8 Q2 Q2 56.767 2016 Soubory v repozitáři: hoschl-0452147.pdf 84949479880 SCOPUS 000366844400011 WOS cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 69 č. 1 2016 72 81 Elsevier