Název:	Evaluation of fuzzy rough set feature selection for content based image retrieval system with noisy images
Autoři:	Shahabi Lotfabadi, Maryam Fairuz Shiratuddin, Mohd Wai Wong, Kok
Citace zdrojového dokumentu:	WSCG 2014: communication papers proceedings: 22nd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 95-102.
Datum vydání:	2014
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	wscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf http://hdl.handle.net/11025/26383
ISBN:	978-80-86943-71-8
Klíčová slova:	fuzzy hrubá množina;obsah založený na systému načítání obrazu;hluk
Klíčová slova v dalším jazyce:	fuzzy rough set;content based image retrieval system;noise
Abstrakt v dalším jazyce:	In this paper Fuzzy Rough Set is used for feature selection in the Content Based Image Retrieval system. Noisy query images are fed to this Content Based Image Retrieval system and the results are compared with four other feature selection methods. The four other feature selection methods are Genetic Algorithm, Information Gain, OneR and Principle Component Analysis. The main objective of this paper is to evaluate the rules which are extracted from fuzzy rough set and determine whether these rules which are used for training the Support Vector Machine can deal with noisy query images as well as the original queried images. To evaluate the Fuzzy Rough set feature selection, we use 10 sematic group images from COREL database which we have purposely placed some defect by adding Gaussian, Poisson and Salt and Pepper noises of different magnitudes. As a result, the proposed method performed better in term of accuracies in most of the different types of noise when compared to the other four feature selection methods.
Práva:	@ Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2014: Communication Papers Proceedings

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