An efficient image descriptor for image classification and CBIR

Pattern recognition and feature extraction of images always have been important subjects in improving the performance of image classification and Content-Based Image Retrieval (CBIR). Recently, Machine Learning and Deep Learning algorithms are utilized widely in order to achieve these targets. In th...

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Vydáno v:Optik (Stuttgart) Ročník 214; s. 164833
Hlavní autoři: Shakarami, Ashkan, Tarrah, Hadis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany Elsevier GmbH 01.07.2020
Témata:
Content-based image retrieval (CBIR)
Convolutional neural network (CNN)
Image classification
Image descriptor
Machine learning
Pattern recognition
Image descriptor
Content-based image retrieval (CBIR)
Pattern recognition
Convolutional neural network (CNN)
Machine learning
Image classification
ISSN:0030-4026, 1618-1336, 0030-4026
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Shrnutí:Pattern recognition and feature extraction of images always have been important subjects in improving the performance of image classification and Content-Based Image Retrieval (CBIR). Recently, Machine Learning and Deep Learning algorithms are utilized widely in order to achieve these targets. In this research, an efficient method for image description is proposed which is developed by Machine Learning and Deep Learning algorithms. This method is created using combination of an improved AlexNet Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) descriptors. Furthermore, the Principle Component Analysis (PCA) algorithm has been used for dimension reduction. The experimental results demonstrate the superiority of the offered method compared to existing methods by improving the accuracy, mean Average Precision (mAP) and decreasing the complex computation. The experiments have been run on Corel-1000, OT and FP datasets.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0030-4026
1618-1336
0030-4026
DOI:10.1016/j.ijleo.2020.164833