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https://hdl.handle.net/20.500.14279/2935| Τίτλος: | Fuzzy ART for Relatively Fast Unsupervised Image Color Quantization | Συγγραφείς: | Shorter, Nicholas S. Kasparis, Takis |
metadata.dc.contributor.other: | Κασπαρής, Τάκης | Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Λέξεις-κλειδιά: | Image Color Quantization;Fuzzy ART;Clustering;Unsupervised | Ημερομηνία Έκδοσης: | Δεκ-2008 | Πηγή: | SSPR /SPR 2008: Structural, Syntactic, and Statistical Pattern Recognition, pp. 147-156, Orlando, USA, December 4-6, 2008. Proceedings | Conference: | Structural, Syntactic, and Statistical Pattern Recognition | Περίληψη: | The use of Fuzzy Adaptive Resonance Theory (FA) is explored for the unsupervised color quantization of a color image. The red, green and blue color component values of a given color image are passed as input instances into FA which then groups similar colors into the same class. The average of all of the colors in a given class then replaces the pixel values whose original colors belonged to that class. The FA unsupervised clustering is capable of realizing color quantization with competitive accuracy and arguably low computation time. | Description: | Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342) | ISBN: | 9783540896883 | ISSN: | 0302-9743 (Print) 1611-3349 (Online) |
DOI: | 10.1007/978-3-540-89689-0_19 | Rights: | © Springer | Type: | Conference Papers | Affiliation: | University of Central Florida | Affiliation: | University of Central Florida | Publication Type: | Peer Reviewed |
| Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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