Assessing the performance of Co-Saliency Detection method using various Deep Neural Networks

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.

Assessing the performance of Co-Saliency Detection method using various Deep Neural Networks Keywords:

CNN , Co-Saliency detection , SGDM , Adam , RMS , VGG19 , Inceptionv3 , ResNet , MobileNet and PoolNet