Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel framework called fuzzy relevance feedback in interactive content-based image retrieval systems. Conventional binary labeling in relevance feedback requires crisp decisions to be made on the relevance of the retrieved images. This is restrictive as user interpretation of image similarity is imprecise and nonstationary in nature and may vary with respect to different information needs and perceptual subjectivity. It is, therefore, inadequate to model the user perception of image similarity with crisp binary logic. In view of this,  propose a soft relevance notion to integrate the users' fuzzy perception of visual contents into the framework of relevance feedback. A progressive fuzzy radial basis function network is proposed to learn the user information need by optimizing a cost function. An efficient gradient descent-based learning strategy is then employed to estimate the underlying network parameters. Experimental results based on a database of 10 000 images demonstrate the effectiveness of the proposed method.
['Kim-Hui Yap', 'Kui Wu']
I am writing a research paper on a idea A soft relevance framework in content-based image retrieval systems
[{"from": "human", "value": "I am writing a research paper on a idea A soft relevance framework in content-based image retrieval systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel framework called fuzzy relevance feedback in interactive content-based image retrieval systems. Conventional binary labeling in relevance feedback requires crisp decisions to be made on the relevance of the retrieved images. This is restrictive as user interpretation of image similarity is imprecise and nonstationary in nature and may vary with respect to different information needs and perceptual subjectivity. It is, therefore, inadequate to model the user perception of image similarity with crisp binary logic. In view of this,  propose a soft relevance notion to integrate the users' fuzzy perception of visual contents into the framework of relevance feedback. A progressive fuzzy radial basis function network is proposed to learn the user information need by optimizing a cost function. An efficient gradient descent-based learning strategy is then employed to estimate the underlying network parameters. Experimental results based on a database of 10 000 images demonstrate the effectiveness of the proposed method."}]