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10.1016/j.ijleo.2020.164833 http://scihub22266oqcxt.onion/10.1016/j.ijleo.2020.164833 32372771!7198219!32372771     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32372771&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Optik+(Stuttg) 2020 ; 214 (?): 164833 Nephropedia Template TP {{tp|p=32372771|t=2020. An efficient image descriptor for image classification and CBIR |pdf=|usr=}}{{32372771}} gab.com Text An efficient image descriptor for image classification and CBIR JO: Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=32372771 #FOAvip 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. Twit Text FOAVip An efficient image descriptor for image classification and CBIR ????Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=32372771 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32372771 #FOAvip English Wikipedia <ref>{{Cite pmid|32372771}}</ref> An efficient image descriptor for image classification and CBIR #MMPMID32372771 Shakarami A; Tarrah H Optik (Stuttg) 2020[Jul]; 214 (?): 164833 PMID32372771show ga 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. ?An efficient image descriptor for image classification and CBIR (epmc).pdf DeepDyve Pubget Overpricing