Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the rapid evolution of Internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One obstacle to high-speed testing is the time cost of feature extraction for test objects, particularly for objects with complex representation such as images, videos and audios. ,  study the problem of reducing test time cost by extracting cheap but sufficient features.  propose the TEFE (time-efficient feature extraction) approach, which balances beten the test accuracy and test time cost by extracting a proper subset of features for each test object. In the implementation, TEFE trains a sequence of support vector machines and classifies each test object cascadingly. Empirical study shows that TEFE is time efficient while holding a classification accuracy close to that of using all features. It also shows that the test time is linearly adjustable in TEFE.
['Liping Liu', 'Yang Yu', 'Yuan Jiang', 'Zhi-Hua Zhou']
I am writing a research paper on a idea TEFE: A Time-Efficient Approach to Feature Extraction
[{"from": "human", "value": "I am writing a research paper on a idea TEFE: A Time-Efficient Approach to Feature Extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the rapid evolution of Internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One obstacle to high-speed testing is the time cost of feature extraction for test objects, particularly for objects with complex representation such as images, videos and audios. ,  study the problem of reducing test time cost by extracting cheap but sufficient features.  propose the TEFE (time-efficient feature extraction) approach, which balances beten the test accuracy and test time cost by extracting a proper subset of features for each test object. In the implementation, TEFE trains a sequence of support vector machines and classifies each test object cascadingly. Empirical study shows that TEFE is time efficient while holding a classification accuracy close to that of using all features. It also shows that the test time is linearly adjustable in TEFE."}]