NDLI: Image classification using SLIC superpixel and FAAGKFCM image segmentation

Content Provider	IET Digital Library
Author	Singh, Nongmeikapam Kishorjit Singh, Ningthoujam Johny Kumar, Wahengbam Kanan
Abstract	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.
Starting Page	487
Ending Page	494
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0255
Journal	IET Image Processing
Publisher Date	2019-10-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatically Adjustable Gaussian Radial Basis Function Kernel-based Fuzzy C-means Caltech 101 Dataset Caltech 256 Dataset Computer Vision Computer Vision And Image Processing Technique Deep Features FAAGKFCM Image Segmentation Feature Extraction Image Classification Image Recognition Image Representation Image Segmentation Interpolation And Function Approximation Iterative Method Knowledge Engineering Technique Learning in AI MIT 67 Image Datasets Neural Computing Technique Numerical Analysis Pattern Clustering Radial Basis Function Network Simple Linear Iterative Clustering Superpixel SLIC Superpixel Speeded Up Robust Feature Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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