Modified ResNet50 model and semantic segmentation based image co-saliency detection

Abstract

Co-salient object identification is a new and emerging branch of the visual saliency detection technique that tries to find salient pattern appearing in several image groups. The proposed work has the potential to benefit a wide variety of important applications including the detection of objects of interest, more robust object recognition and animation synthesis, handling input image query, 3D object reconstruction, object co-segmentation etc. To build modified ResNet50 model, the hyperparameters are adjusted in the current work to increase accuracy while minimizing loss. The modified network is trained on HOG features to mine more significant features along with their corresponding ground truth images. For a more streamlined outcome, the proposed system was built using the SGDM optimizer. During testing among the relevant and irrelevant image the network generates appropriate co-saliency map of relevant images. Integrating the associated and prominent characteristics of the image yields the appropriate ground truth for each image. The proposed method reports better F1 value 98.7% and MAE score 0.089 value when compared with SOTA model.