research-article

An Empirical Study on Feature Extraction for the Classification of Textural and Natural Images

Authors:

Syed Ali,

Chih-Cheng HungAuthors Info & Claims

RACS '16: Proceedings of the International Conference on Research in Adaptive and Convergent Systems

Pages 51 - 55

https://doi.org/10.1145/2987386.2987419

Published: 11 October 2016 Publication History

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Abstract

Most image classification algorithms consist of feature extraction and image classification. Hence, feature extraction is a critical step for obtaining an accurate classification result. In this study, we perform the classification experiments based on the image features extracted using Local Binary Pattern operator (LBP), Discrete Wavelet Transforms (DWT) and Color Features (CF) for image classification. A comparison is made using the different combination of features; 1) LBP features only, 2) LBP and DWT features, and 3) LBP, DWT and CF features with a slight modification. Our goal is to determine what types of features are useful for improving the classification results. The Linear Support Vector Machine (SVM) algorithm is used to classify each pixel into a class based on the features used to obtain a classified image. Preliminary experimental results show that the hybrid of DWT, LBP, and CF gives the highest accuracy.

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https://doi.org/10.1109/ICCCS49678.2020.9277141
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https://doi.org/10.1109/SERA.2018.8477230

An Empirical Study on Feature Extraction for the Classification of Textural and Natural Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

RACS '16: Proceedings of the International Conference on Research in Adaptive and Convergent Systems

October 2016

266 pages

ISBN:9781450344555

DOI:10.1145/2987386

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 October 2016

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SIGAPP
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RACS '16: International Conference on Research in Adaptive and Convergent Systems

October 11 - 14, 2016

Odense, Denmark

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RACS '16 Paper Acceptance Rate 40 of 161 submissions, 25%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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November 5 - 8, 2024

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Zhang YGong QDing WJia S(2020)An HTTP Video Stream Identification Method Based on Wavelet Packet Analysis and SVM2020 5th International Conference on Computing, Communication and Security (ICCCS)10.1109/ICCCS49678.2020.9277141(1-6)Online publication date: 14-Oct-2020
https://doi.org/10.1109/ICCCS49678.2020.9277141
Du ZMa LLi HLi QSun GLiu Z(2018)Network Traffic Anomaly Detection Based on Wavelet Analysis2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA.2018.8477230(94-101)Online publication date: Jun-2018
https://doi.org/10.1109/SERA.2018.8477230

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Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

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