Title :
Extraction of memory colors using Bayesian Networks
Author :
Jaber, Mustafa ; Saber, Eli ; Sahin, Ferat
Author_Institution :
Chester F. Carlson Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
Abstract :
In this work, a region classification algorithm based on low-level features and probabilistic framework is proposed where skin, sky, and vegetation memory color classes are detected in digital images. A region´s low-level features are extracted using a segmentation map of input image. Bayesian network (BN) is used to classify memory color regions for smart rendering in printing applications. Other applications of the proposed technique include image annotation, indexing, and content retrieval. The algorithm was tested on a large database of color images with 85% classification accuracy.
Keywords :
belief networks; digital printing; feature extraction; image colour analysis; image segmentation; rendering (computer graphics); Bayesian networks; content retrieval; digital images; image annotation; image segmantation map; indexing; low-level features extraction; memory colors extraction; printing applications; region classification algorithm; Bayesian methods; Classification algorithms; Digital images; Feature extraction; Image segmentation; Indexing; Printing; Rendering (computer graphics); Skin; Vegetation mapping; Bayesian Networks; image understanding; memory color; region classification;
Conference_Titel :
System of Systems Engineering, 2009. SoSE 2009. IEEE International Conference on
Conference_Location :
Albuquerque, NM
Print_ISBN :
978-1-4244-4766-4
Electronic_ISBN :
978-1-4244-4767-1