Feature Extraction: A Survey of the Types, Techniques, Applications

Feature extraction (FE) is an important step in image retrieval, image processing, data mining and computer vision. FE is the process of extracting relevant information from raw data. However, the problem of extracting appropriate features that can reflect the intrinsic content of a piece of data or...

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Bibliographic Details
Published inInternational Conference on Signal Processing and Communication (Online) pp. 158 - 164
Main Authors Salau, Ayodeji Olalekan, Jain, Shruti
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2019
Subjects
application
data
Data mining
Feature extraction
Hidden Markov models
Image color analysis
image processing
Image retrieval
Iron
mining
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ISBN9781538694350
1538694352
ISSN2643-444X
DOI10.1109/ICSC45622.2019.8938371

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Summary:Feature extraction (FE) is an important step in image retrieval, image processing, data mining and computer vision. FE is the process of extracting relevant information from raw data. However, the problem of extracting appropriate features that can reflect the intrinsic content of a piece of data or dataset as complete as possible is still a challenge for most FE techniques. In this paper, we present a survey of the existing FE techniques used in recent times. In this study, it was observed that the most unique features that can be extracted when using GLDS features on images are contrast, homogeneity, entropy, mean and energy. In addition, it was observed that FE techniques are not mainly application specific but can be applied to several applications.
ISBN:9781538694350
1538694352
ISSN:2643-444X
DOI:10.1109/ICSC45622.2019.8938371