Open AccessImage classification using SLIC superpixel and FAAGKFCM image segmentation
Author(s) -
Kishorjit Singh gmeikapam,
Johny Singh Ningthoujam,
Kanan Kumar Wahengbam
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0255
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , image segmentation , kernel (algebra) , feature (linguistics) , cluster analysis , contextual image classification , segmentation , image (mathematics) , feature extraction , radial basis function , computer vision , artificial neural network , mathematics , linguistics , philosophy , combinatorics
Image classification is one of the popular fields for researchers in computer vision. This study highlights the use of simple linear iterative clustering (SLIC) superpixel in combination with fast and automatically adjustable Gaussian radial basis function kernel‐based fuzzy C‐means (FAAGKFCM) for image segmentation along with the deep learning techniques. Bag‐of‐feature with speeded up robust feature along with deep features are used for classification of 101 classes of the image and 256 classes of the image from Caltech 101, Caltech 256 and MIT 67 image datasets. The combination of SLIC superpixel with FAAGKFCM image segmentation acts as the pre‐processing step for image classification, which in turn provides a better result in the classification of images. This method has achieved an accuracy of 94% in Caltech 101 dataset, 85% in Caltech 256 dataset and 84% in MIT 67 dataset.