Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classifying video based on its content often requires learning a classifier from labeled samples. Sometimes the semantic labels available for a video refer to the class or category of the video as a whole; whereas the discriminative features that result in that categorization may only occur over a temporal subset of the video and may occur anywhere, leading to sub-optimal performance.  therefore need to simultaneously learn discriminative features and their temporal support while remaining independent of position within the video.  propose a solution to this label-resolution problem using a wavelet decomposition of frame-level feature time-series follod by learning discrimin ative extrema values over multiple time scales.  apply this approach to automatically detecting the presence of adult content in online videos with a resulting equal-error rate around 5%.
['Ullas Gargi', 'Jay Yagnik']
I am writing a research paper on a idea Solving the label resolution problem in supervised video content classification
[{"from": "human", "value": "I am writing a research paper on a idea Solving the label resolution problem in supervised video content classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classifying video based on its content often requires learning a classifier from labeled samples. Sometimes the semantic labels available for a video refer to the class or category of the video as a whole; whereas the discriminative features that result in that categorization may only occur over a temporal subset of the video and may occur anywhere, leading to sub-optimal performance.  therefore need to simultaneously learn discriminative features and their temporal support while remaining independent of position within the video.  propose a solution to this label-resolution problem using a wavelet decomposition of frame-level feature time-series follod by learning discrimin ative extrema values over multiple time scales.  apply this approach to automatically detecting the presence of adult content in online videos with a resulting equal-error rate around 5%."}]