Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a framework that performs automatic semantic annotation of visual events (SAVE). This is an enabling technology for content-based video annotation, query and retrieval with applications in Internet video search and video data mining. The method involves identifying objects in the scene, describing their inter-relations, detecting events of interest, and representing them semantically in a human readable and query-able format. The SAVE framework is composed of three main components. The first component is an image parsing engine that performs scene content extraction using bottom-up image analysis and a stochastic attribute image grammar, where  define a visual vocabulary from pixels, primitives, parts, objects and scenes, and specify their spatio-temporal or compositional relations; and a bottom-up top-down strategy is used for inference. The second component is an event inference engine, where the video event markup language (VEML) is adopted for semantic representation, and a grammar-based approach is used for event analysis and detection. The third component is the text generation engine that generates text report using head-driven phrase structure grammar (HPSG). The main contribution of this paper is a framework for an end-to-end system that infers visual events and annotates a large collection of videos. Experiments with maritime and urban scenes indicate the feasibility of the proposed approach.
['Mun Wai Lee', 'Asaad Hakeem', 'Niels Haering', 'Song-Chun Zhu']
I am writing a research paper on a idea SAVE: A framework for semantic annotation of visual events
[{"from": "human", "value": "I am writing a research paper on a idea SAVE: A framework for semantic annotation of visual events"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a framework that performs automatic semantic annotation of visual events (SAVE). This is an enabling technology for content-based video annotation, query and retrieval with applications in Internet video search and video data mining. The method involves identifying objects in the scene, describing their inter-relations, detecting events of interest, and representing them semantically in a human readable and query-able format. The SAVE framework is composed of three main components. The first component is an image parsing engine that performs scene content extraction using bottom-up image analysis and a stochastic attribute image grammar, where  define a visual vocabulary from pixels, primitives, parts, objects and scenes, and specify their spatio-temporal or compositional relations; and a bottom-up top-down strategy is used for inference. The second component is an event inference engine, where the video event markup language (VEML) is adopted for semantic representation, and a grammar-based approach is used for event analysis and detection. The third component is the text generation engine that generates text report using head-driven phrase structure grammar (HPSG). The main contribution of this paper is a framework for an end-to-end system that infers visual events and annotates a large collection of videos. Experiments with maritime and urban scenes indicate the feasibility of the proposed approach."}]