Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Previous video summarization studies focused on monocular videos, and the results would not be good if they re applied to multi-view videos directly, due to problems such as the redundancy in multiple views. ,  present a method for summarizing multi-view videos.  construct a spatio-temporal shot graph and formulate the summarization problem as a graph labeling task. The spatio-temporal shot graph is derived from a hypergraph, which encodes the correlations with different attributes among multi-view video shots in hyperedges.  then partition the shot graph and identify clusters of event-centered shots with similar contents via random walks. The summarization result is generated through solving a multi-objective optimization problem based on shot importance evaluated using a Gaussian entropy fusion scheme. Different summarization objectives, such as minimum summary length and maximum information coverage, can be accomplished in the framework. Moreover, multi-level summarization can be achieved easily by configuring the optimization parameters.  also propose the multi-view storyboard and event board for presenting multi-view summaries. The storyboard naturally reflects correlations among multi-view summarized shots that describe the same important event. The event-board serially assembles event-centered multi-view shots in temporal order. Single video summary which facilitates quick browsing of the summarized multi-view video can be easily generated based on the event board representation.
['Yanwei Fu', 'Yanwen Guo', 'Yanshu Zhu', 'Feng Liu', 'Chuan-Ming Song', 'Zhi-Hua Zhou']
I am writing a research paper on a idea Multi-View Video Summarization
[{"from": "human", "value": "I am writing a research paper on a idea Multi-View Video Summarization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Previous video summarization studies focused on monocular videos, and the results would not be good if they re applied to multi-view videos directly, due to problems such as the redundancy in multiple views. ,  present a method for summarizing multi-view videos.  construct a spatio-temporal shot graph and formulate the summarization problem as a graph labeling task. The spatio-temporal shot graph is derived from a hypergraph, which encodes the correlations with different attributes among multi-view video shots in hyperedges.  then partition the shot graph and identify clusters of event-centered shots with similar contents via random walks. The summarization result is generated through solving a multi-objective optimization problem based on shot importance evaluated using a Gaussian entropy fusion scheme. Different summarization objectives, such as minimum summary length and maximum information coverage, can be accomplished in the framework. Moreover, multi-level summarization can be achieved easily by configuring the optimization parameters.  also propose the multi-view storyboard and event board for presenting multi-view summaries. The storyboard naturally reflects correlations among multi-view summarized shots that describe the same important event. The event-board serially assembles event-centered multi-view shots in temporal order. Single video summary which facilitates quick browsing of the summarized multi-view video can be easily generated based on the event board representation."}]