Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel approach to spatio-temporal saliency detection in video is proposed. Saliency computation is considered as an optimization problem that maximizes the energy of a fully-connected graphical model based on spatio-temporal feature distinctiveness. Each pixel in a video is modeled by a node, and the spatio-temporal feature distinctiveness beten pixels by edges connecting the nodes in the graph. The computational complexity is addressed by compressing the fully-connected graph into an abstracted, fully-connected graph with far fer nodes, where each node in the new graph characterizes nodal groups. The saliency value of each pixel is then computed based on spatio-temporal feature distinctiveness and the energy representation of its nodal group given the constructed graphical model. Experimental results show that our approach outperforms existing approaches to spatio-temporal salient region detection.
['Amir-Hossein Karimi', 'Mohammad Javad Shafiee', 'Christian Scharfenberger', 'Imen Bendaya', 'Shahid A. Haider', 'Nayan Talukdar1', 'David A. Clausi', 'Alexander Wong']
I am writing a research paper on a idea Spatio-temporal saliency detection using abstracted fully-connected graphical models
[{"from": "human", "value": "I am writing a research paper on a idea Spatio-temporal saliency detection using abstracted fully-connected graphical models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel approach to spatio-temporal saliency detection in video is proposed. Saliency computation is considered as an optimization problem that maximizes the energy of a fully-connected graphical model based on spatio-temporal feature distinctiveness. Each pixel in a video is modeled by a node, and the spatio-temporal feature distinctiveness beten pixels by edges connecting the nodes in the graph. The computational complexity is addressed by compressing the fully-connected graph into an abstracted, fully-connected graph with far fer nodes, where each node in the new graph characterizes nodal groups. The saliency value of each pixel is then computed based on spatio-temporal feature distinctiveness and the energy representation of its nodal group given the constructed graphical model. Experimental results show that our approach outperforms existing approaches to spatio-temporal salient region detection."}]