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Soft Computing

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23.01.2019 | Focus

Performance analysis of soft computing techniques for the automatic classification of fruits dataset

verfasst von: L. Rajasekar, D. Sharmila

Erschienen in: Soft Computing | Ausgabe 8/2019

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Abstract

Different properties of numerous types of fruits and vegetable classification are still an intricate task. The soft computing strategies are used to recognize a fruit by blending the three basic features which characterize the object: color, shape and texture. The classifiers are relatively effective, when the image feature vector is fused with one another. This technique decreases the dimensionality of the feature vector. So the combined and normalized features of the image are producing better classification accuracy with minimum number of training data. K-nearest neighbor (K-NN), linear discriminant analysis, naive Bayes, error-correcting output classifier and decision tree classifiers are used for image recognition process. A tenfold cross-validation technique is used to improve the classification accuracy of the classifier. The experiment is demonstrated in all the five techniques with 2400 images from the 24 categories of fruits and vegetables. The K-NN scored 97.5% of classification accuracy.

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Titel: Performance analysis of soft computing techniques for the automatic classification of fruits dataset
verfasst von: L. Rajasekar
D. Sharmila
Publikationsdatum: 23.01.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 8/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-03776-z

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