A Comparative Study of Feature Extraction Methods in Images Classification
Автор: Seyyid Ahmed Medjahed
Статья в выпуске: 3 vol.7, 2015 года.
Feature extraction is an important step in image classification. It allows to represent the content of images as perfectly as possible. However, in this paper, we present a comparison protocol of several feature extraction techniques under different classifiers. We evaluate the performance of feature extraction techniques in the context of image classification and we use both binary and multiclass classifications. The analyses of performance are conducted in term of: classification accuracy rate, recall, precision, f-measure and other evaluation measures. The aim of this research is to show the relevant feature extraction technique that improves the classification accuracy rate and provides the most implicit classification data. We analyze the models obtained by each feature extraction method under each classifier.
Feature extraction, Image classification, Models evaluation, Support vector machine
Короткий адрес: https://readera.ru/15013533
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