Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In automatic video summarization, visual summary is constructed typically based on the analysis of low-level features with little consideration of video semantics. Hover, the contextual and semantic information of a video is marginally related to low-level features in practice although they are useful to compute visual similarity beten frames. Therefore,  propose a novel video summarization technique, where the semantically important information is extracted from a set of keyframes given by human and the summary of a video is constructed based on the automatic temporal segmentation using the analysis of inter-frame similarity to the keyframes. Toward this goal,  model a video sequence with a dissimilarity matrix based on bidirectional similarity measure beten every pair of frames, and subsequently characterize the structure of the video by a nonlinear manifold embedding. Then,  formulate video summarization as a variant of the 0–1 knapsack problem, which is solved by dynamic programming efficiently. The effectiveness of our algorithm is illustrated quantitatively and qualitatively using realistic videos collected from YouTube.
['Bohyung Han', 'Jihun Hamm', 'Jack Sim']
I am writing a research paper on a idea Personalized video summarization with human in the loop
[{"from": "human", "value": "I am writing a research paper on a idea Personalized video summarization with human in the loop"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In automatic video summarization, visual summary is constructed typically based on the analysis of low-level features with little consideration of video semantics. Hover, the contextual and semantic information of a video is marginally related to low-level features in practice although they are useful to compute visual similarity beten frames. Therefore,  propose a novel video summarization technique, where the semantically important information is extracted from a set of keyframes given by human and the summary of a video is constructed based on the automatic temporal segmentation using the analysis of inter-frame similarity to the keyframes. Toward this goal,  model a video sequence with a dissimilarity matrix based on bidirectional similarity measure beten every pair of frames, and subsequently characterize the structure of the video by a nonlinear manifold embedding. Then,  formulate video summarization as a variant of the 0\u20131 knapsack problem, which is solved by dynamic programming efficiently. The effectiveness of our algorithm is illustrated quantitatively and qualitatively using realistic videos collected from YouTube."}]