Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present an index structure-based method to fast and robustly search short video clips in large video collections. First  temporally segment a given long video stream into overlapped matching windows, then map extracted features from the windows into points in a high dimensional feature space, and construct index structures for these feature points for querying process. Different from linear-scan similarity matching methods, querying process can be accelerated by spatial pruning brought by an index structure. A multi-resolution kd-tree (mrkd-tree) is employed to complete exact K-NN Query and range query with the aim of fast and precisely searching out all short video segments having the same contents as the query. In terms of feature representation, rather than selecting representative key frames,  develop a set of spatial-temporal features in order to globally capture the pattern of a short video clip (e.g. a commercial clip, a lead in/out clip) and combine it with the color range feature to form video signatures. Our experiments have shown the efficiency and effectiveness of the proposed method that the very first instance of a given 10-sec query clip can be identified from a 10.5-hour video collection in tens of milliseconds. The proposed method has been also compared with the fast sequential search algorithm
['Junsong Yuan', 'Ling-Yu Duan', 'Qi Tian', 'Changsheng Xu']
I am writing a research paper on a idea Fast and robust short video clip search using an index structure
[{"from": "human", "value": "I am writing a research paper on a idea Fast and robust short video clip search using an index structure"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present an index structure-based method to fast and robustly search short video clips in large video collections. First  temporally segment a given long video stream into overlapped matching windows, then map extracted features from the windows into points in a high dimensional feature space, and construct index structures for these feature points for querying process. Different from linear-scan similarity matching methods, querying process can be accelerated by spatial pruning brought by an index structure. A multi-resolution kd-tree (mrkd-tree) is employed to complete exact K-NN Query and range query with the aim of fast and precisely searching out all short video segments having the same contents as the query. In terms of feature representation, rather than selecting representative key frames,  develop a set of spatial-temporal features in order to globally capture the pattern of a short video clip (e.g. a commercial clip, a lead in/out clip) and combine it with the color range feature to form video signatures. Our experiments have shown the efficiency and effectiveness of the proposed method that the very first instance of a given 10-sec query clip can be identified from a 10.5-hour video collection in tens of milliseconds. The proposed method has been also compared with the fast sequential search algorithm"}]