Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Video indexing based on contents annotations can fully explore semantic information of video data. Hover, the most difficult and time-consuming process in annotation-based indexing is to identify appropriate video intervals for various semantic contents manually. Thus, automatic discovering video intervals from video data will be helpful for the indexing work. For this purpose,  propose semantic structures of video data and a mechanism for discovering semantic structures. The basic concept of our approach is to (1) discover consecutive sequences of shots from video data, each of which represents a consistent action or situation, and (2) index each of the discovered video intervals based on its semantics. A semantic structure is a collection of discovered video intervals that are classified into three categories: unchanged (i.e. actors or backgrounds are unchanged throughout the interval), gradually changing (i.e. actors or backgrounds are changing shot by shot) and multiplexing (i.e. individual actors or backgrounds are appearing by turns). The mechanism discovers these types of video intervals by comparing and contrasting similarity beten each shot, and indexes each of discovered intervals by using indexing algorithms prepared for each type.  show how ll our approach works for identifying video intervals with some experimental results.
['Kohji Zettsu', 'Kuniaki Uehara', 'Katsumi Tanaka']
I am writing a research paper on a idea Semantic structures for video data indexing
[{"from": "human", "value": "I am writing a research paper on a idea Semantic structures for video data indexing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Video indexing based on contents annotations can fully explore semantic information of video data. Hover, the most difficult and time-consuming process in annotation-based indexing is to identify appropriate video intervals for various semantic contents manually. Thus, automatic discovering video intervals from video data will be helpful for the indexing work. For this purpose,  propose semantic structures of video data and a mechanism for discovering semantic structures. The basic concept of our approach is to (1) discover consecutive sequences of shots from video data, each of which represents a consistent action or situation, and (2) index each of the discovered video intervals based on its semantics. A semantic structure is a collection of discovered video intervals that are classified into three categories: unchanged (i.e. actors or backgrounds are unchanged throughout the interval), gradually changing (i.e. actors or backgrounds are changing shot by shot) and multiplexing (i.e. individual actors or backgrounds are appearing by turns). The mechanism discovers these types of video intervals by comparing and contrasting similarity beten each shot, and indexes each of discovered intervals by using indexing algorithms prepared for each type.  show how ll our approach works for identifying video intervals with some experimental results."}]