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مدیریت فناوری اطلاعات، جلد ۱۵، شماره Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-۲۰۲۲)، صفحات ۲۳-۳۴
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عنوان انگلیسی Assessing the performance of Co-Saliency Detection method using various Deep Neural Networks
چکیده انگلیسی مقاله Co-Saliency object detection is the process of identifying common and repetitive objects from the group of images. Earlier studies have looked over several state-of-art deep neural network methodologies for co-saliency detection approach. The Deep CNN approaches rely heavily on co-saliency detection due to their potent feature extraction capabilities both deep and wide. This article assess the performance of several state-of-art deep learning model (VGG19, Inceptionv3, modifiedResNet, MobileNetV2 and PoolNet) for the purpose of co-saliency detection among images from benchmark datasets. All the models were trained on   70% part of the dataset and remaining were used for testing purpose. Experimental results show that modified ResNetmodel outperforms getting 96.53% accuracy as compared to other state-of-the-art deep neural network models.
کلیدواژه‌های انگلیسی مقاله CNN, Co-Saliency detection, SGDM, Adam, RMS, VGG19, Inceptionv3, ResNet, MobileNet and PoolNet
نویسندگان مقاله Anuj Mangal |
Department of Computer Engineering & Applications, GLA University, Mathura.

Hitendra Garg |
Department of Computer Engineering & Applications, GLA University, Mathura.

Charul Bhatnagar |
Department of Computer Engineering & Applications, GLA University, Mathura.

نشانی اینترنتی https://jitm.ut.ac.ir/article_95243_abe0c3af8339146e590477fdaa46c9fa.pdf
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