Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Based on the local keypoints extracted as salient image patches, an image can be described as a ?bag-of-visual-words (BoW)? and this representation has appeared promising for object and scene classification. The performance of BoW features in semantic concept detection for large-scale multimedia databases is subject to various representation choices. ,  conduct a comprehensive study on the representation choices of BoW, including vocabulary size, ighting scheme, stop word removal, feature selection, spatial information, and visual bi-gram.  offer practical insights in how to optimize the performance of BoW by choosing appropriate representation choices. For the ighting scheme,  elaborate a soft-ighting method to assess the significance of a visual word to an image.  experimentally show that the soft-ighting outperforms other popular ighting schemes such as TF-IDF with a large margin. Our extensive experiments on TRECVID data sets also indicate that BoW feature alone, with appropriate representation choices, already produces highly competitive concept detection performance. Based on our empirical findings,  further apply our method to detect a large set of 374 semantic concepts. The detectors, as ll as the features and detection scores on several recent benchmark data sets, are released to the multimedia community.
['Yu-Gang Jiang', 'Jun Yang', 'Chong-Wah Ngo', 'Alexander G. Hauptmann']
I am writing a research paper on a idea Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
[{"from": "human", "value": "I am writing a research paper on a idea Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Based on the local keypoints extracted as salient image patches, an image can be described as a ?bag-of-visual-words (BoW)? and this representation has appeared promising for object and scene classification. The performance of BoW features in semantic concept detection for large-scale multimedia databases is subject to various representation choices. ,  conduct a comprehensive study on the representation choices of BoW, including vocabulary size, ighting scheme, stop word removal, feature selection, spatial information, and visual bi-gram.  offer practical insights in how to optimize the performance of BoW by choosing appropriate representation choices. For the ighting scheme,  elaborate a soft-ighting method to assess the significance of a visual word to an image.  experimentally show that the soft-ighting outperforms other popular ighting schemes such as TF-IDF with a large margin. Our extensive experiments on TRECVID data sets also indicate that BoW feature alone, with appropriate representation choices, already produces highly competitive concept detection performance. Based on our empirical findings,  further apply our method to detect a large set of 374 semantic concepts. The detectors, as ll as the features and detection scores on several recent benchmark data sets, are released to the multimedia community."}]