Multi-class SVM based classification approach for tomato ripeness

Advances in Intelligent Systems and Computing

Volumn 237, Issue , 2014, Pages 175-186

  Elhariri, Esraa ^a   El Bendary, Nashwa ^b   Fouad, Mohamed Mostafa M ^b   PlatoŠ, Jan ^c   Hassanien, Aboul Ella ^d   Hussein, Ahmed M M ^e  

^a FAYOUM UNIVERSITY   (Egypt)

^b Scientific Research Group in Egypt ^*  (Egypt)

^c VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

^d CAIRO UNIVERSITY   (Egypt)

^e MINIA UNIVERSITY   (Egypt)

Author keywords

Features extraction; Image classification; Principal component analysis (PCA); Ripeness; Support vector machine (SVM)

Indexed keywords

CLASSIFICATION (OF INFORMATION); EXTRACTION; FEATURE EXTRACTION; FRUITS; IMAGE PROCESSING; MACHINE COMPONENTS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CLASSIFICATION APPROACH; CONTENT BASED IMAGE CLASSIFICATION; FEATURE EXTRACTION AND CLASSIFICATION; FEATURES EXTRACTION; PRINCIPAL COMPONENTS ANALYSIS; RIPENESS; SUPPORT VECTOR MACHINE ALGORITHM;

IMAGE CLASSIFICATION;

EID: 84927731744     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-01781-5_17     Document Type: Conference Paper

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