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Original scientific paper

A Mighty Image Retrieval Descriptor Based on Machine Learning and Gaussian Derivative Filter

El Aroussi El Mehdi ; Chouaib Doukkali University, ELITES Laboratory, Departement of Computer Science and Mathematics Higher School of Technology El Jadida, Morocco *
Barakat Latifa ; Chouaib Doukkali University, Management of Sustainable Agriculture Laboratory, Higher School of Technology El Jadida, Morocco
Silkan Hassan orcid id ; Chouaib Doukkali University, LaROSERI Laboratory, Department of computer sciences, Faculty of Sciences, El Jadida, Morocco

* Corresponding author.

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The development of new image descriptor has always been an important topic to improve the efficiency of content- based image classification and retrieval. Improvements and developments in machine learning and deep learning algorithms as well as artificial intelligence algorithms are widely used by researchers to obtain effective CBIR descriptors. In our article, we will present a robust image descriptor, extended by machine learning and deep learning algorithms. The descriptor is provided through a Gaussian derivative filter scaffold named GDF-HOG with an enhanced convolutional neural network (CNN) AlexNet, to reduce the dimensions we used the principal component analysis algorithm. The experimental results were carried out on Oliva and Torralba, Caltech-101, Wang and Coil100 datasets. Experiments show that the accuracy of the proposed method is 98.23% for Coil-100%, 95.92% for Corel-1000, value 87.17 and 94.6% for Oliva and Torralba. In comparison our results with other descriptor image classifiers show that they achieved accuracy increases of 0.12% on average and up to 3.23%. These experimental results affirm the advantage of the proposed descriptor over existing systems based in terms of average accuracy. the proposed descriptor improves the precision, and also reduces the complexity of the calculation.


Federated Learning; Machine Learning; Deep Learning; Privacy; Collaborative Machine Learning;

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