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Assessing the performance of Co-Saliency Detection method using various Deep Neural Networks | ||
| Journal of Information Technology Management | ||
| دوره 15، Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-2022)، 2023، صفحه 23-34 اصل مقاله (1.52 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2023.95243 | ||
| نویسندگان | ||
| Anuj Mangal* ؛ Hitendra Garg؛ Charul Bhatnagar | ||
| Department of Computer Engineering & Applications, GLA University, Mathura. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| CNN؛ Co-Saliency detection؛ SGDM؛ ADAM؛ RMS؛ VGG19؛ Inceptionv3؛ ResNet؛ MobileNet and PoolNet | ||
| مراجع | ||
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