Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Accessing, organizing, and manipulating home videos constitutes a technical challenge due to their unrestricted content and the lack of storyline. ,  present a methodology for structuring consumer video, based on the development of statistical models of similarity and adjacency beten video segments in a probabilistic formulation. Learned Gaussian mixture models of inter-segment visual similarity, temporal adjacency, and segment duration are used to represent the classconditional densities of observed features. Such models are then used in a sequential merging algorithm consisting of a binary Bayes classifier, where the merging order is determined by a variation of Highest Confidence First (HCF), and the merging criterion is Maximum a Posteriori (MAP). The merging algorithm can be efficiently implemented and does not need any empirical parameter determination. Finally, the representation of the merging sequence by a tree provides for hierarchical, nonlinear access to the video content. Results on an eight-hour home video database illustrate the validity of our approach.
['Daniel Gatica-Perez', 'Ming-Ting Sun', 'Alexander C. Loui']
I am writing a research paper on a idea Consumer video structuring by probabilistic merging of video segments
[{"from": "human", "value": "I am writing a research paper on a idea Consumer video structuring by probabilistic merging of video segments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Accessing, organizing, and manipulating home videos constitutes a technical challenge due to their unrestricted content and the lack of storyline. ,  present a methodology for structuring consumer video, based on the development of statistical models of similarity and adjacency beten video segments in a probabilistic formulation. Learned Gaussian mixture models of inter-segment visual similarity, temporal adjacency, and segment duration are used to represent the classconditional densities of observed features. Such models are then used in a sequential merging algorithm consisting of a binary Bayes classifier, where the merging order is determined by a variation of Highest Confidence First (HCF), and the merging criterion is Maximum a Posteriori (MAP). The merging algorithm can be efficiently implemented and does not need any empirical parameter determination. Finally, the representation of the merging sequence by a tree provides for hierarchical, nonlinear access to the video content. Results on an eight-hour home video database illustrate the validity of our approach."}]