Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent years have witnessed growing interests in computing compact binary codes and binary visual descriptors to alleviate the heavy computational costs in large-scale visual research. Hover, it is still computationally expensive to linearly scan the large-scale databases for nearest neighbor NN search. In [15], a new approximate NN search algorithm is presented. With the concept of bridge vectors which correspond to the cluster centers in Product Quantization [10] and the augmented neighborhood graph, it is possible to adopt an extract-on-demand strategy on the online querying stage to search with priority.  generalizes the algorithm to the Hamming space with an alternative version of k-means clustering. Despite the simplicity, our approach achieves competitive performance compared to the state-of-the-art methods, i.e., MIH and FLANN, in the aspects of search precision, accessed data volume and average querying time.
['Z.B. Jiang', 'Lingxi Xie', 'Xiaotie Deng', 'Weiwei Xu', 'Jingdong Wang']
I am writing a research paper on a idea Fast Nearest Neighbor Search in the Hamming Space
[{"from": "human", "value": "I am writing a research paper on a idea Fast Nearest Neighbor Search in the Hamming Space"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent years have witnessed growing interests in computing compact binary codes and binary visual descriptors to alleviate the heavy computational costs in large-scale visual research. Hover, it is still computationally expensive to linearly scan the large-scale databases for nearest neighbor NN search. In [15], a new approximate NN search algorithm is presented. With the concept of bridge vectors which correspond to the cluster centers in Product Quantization [10] and the augmented neighborhood graph, it is possible to adopt an extract-on-demand strategy on the online querying stage to search with priority.  generalizes the algorithm to the Hamming space with an alternative version of k-means clustering. Despite the simplicity, our approach achieves competitive performance compared to the state-of-the-art methods, i.e., MIH and FLANN, in the aspects of search precision, accessed data volume and average querying time."}]