Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  summarize our results for the high-level feature extraction task at TRECVID 2009. Our last year’s high-level feature extraction system relied on low-level features as ll as on state-of-theart approaches for camera motion estimation, text detection, face detection and audio segmentation. Based on the observation that the use of face detection results improved the performance of several face related concepts,  have incorporated further specialized object detectors. Using specialized object detectors trained on separate public data sets, objectbased features are generated by assembling detection results to object sequences. A shot-based confidence score and additional features, such as position, frame coverage and movement, are computed for each object class. The object detectors are used for two purposes: (a) to provide retrieval results for concepts directly related to the object class (such as using the boat detector for the concept boat), (b) to provide objectbased features as additional input for the SVM-based concept classifiers. Thus, other related concepts can also profit from object-based features. Furthermore,  investigated the use of SURF (Speeded Up Robust Features). The use of object-based features improved the high-level feature extraction results significantly. Our best run achieved a mean inferred average precision of 9.53%. 1. Structured Abstract The results of our participation in the high-level feature extraction task are presented in this section in the form of the requested structured abstract. In the following sections,  describe our system for high-level feature extraction along with the experimental results. In section 2  describe the extracted features focusing on object-based features and interest point features. The entire high-level feature extraction system is
['Markus Mühling', 'Ralph Ewerth', 'Thilo Stadelmann', 'Bing Shi', 'Bernd Freisleben']
I am writing a research paper on a idea University of Marburg at TRECVID 2008: High-Level Feature Extraction.
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