Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. Hover, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback, (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples, and (3) The high feature dimensions compared to the size of the training set causes over fitting.  propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach.
['Sirikunya Nilpanich', 'Kien A. Hua', 'Antoniya Petkova', 'Yao Hua Ho']
I am writing a research paper on a idea A Lazy Processing Approach to User Relevance Feedback for Content-Based Image Retrieval
[{"from": "human", "value": "I am writing a research paper on a idea A Lazy Processing Approach to User Relevance Feedback for Content-Based Image Retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. Hover, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback, (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples, and (3) The high feature dimensions compared to the size of the training set causes over fitting.  propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach."}]