Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel clustering algorithm for tagging a face dataset (e. g., a personal photo album). The core of the algorithm is a new dissimilarity, called Rank-Order distance, which measures the dissimilarity beten two faces using their neighboring information in the dataset. The Rank-Order distance is motivated by an observation that faces of the same person usually share their top neighbors. Specifically, for each face,  generate a ranking order list by sorting all other faces in the dataset by absolute distance (e. g., L1 or L2 distance beten extracted face recognition features). Then, the Rank-Order distance of two faces is calculated using their ranking orders. Using the new distance, a Rank-Order distance based clustering algorithm is designed to iteratively group all faces into a small number of clusters for effective tagging. The proposed algorithm outperforms competitive clustering algorithms in term of both precision/recall and efficiency.
['Chunhui Zhu', 'Fang Wen', 'Jian Sun']
I am writing a research paper on a idea A rank-order distance based clustering algorithm for face tagging
[{"from": "human", "value": "I am writing a research paper on a idea A rank-order distance based clustering algorithm for face tagging"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel clustering algorithm for tagging a face dataset (e. g., a personal photo album). The core of the algorithm is a new dissimilarity, called Rank-Order distance, which measures the dissimilarity beten two faces using their neighboring information in the dataset. The Rank-Order distance is motivated by an observation that faces of the same person usually share their top neighbors. Specifically, for each face,  generate a ranking order list by sorting all other faces in the dataset by absolute distance (e. g., L1 or L2 distance beten extracted face recognition features). Then, the Rank-Order distance of two faces is calculated using their ranking orders. Using the new distance, a Rank-Order distance based clustering algorithm is designed to iteratively group all faces into a small number of clusters for effective tagging. The proposed algorithm outperforms competitive clustering algorithms in term of both precision/recall and efficiency."}]