Name
Abstract | ||
|---|---|---|
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. However, 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. This paper 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. |
Year | Venue | Field |
|---|---|---|
2016 | MMM | k-nearest neighbors algorithm,Search algorithm,Pattern recognition,Computer science,Best bin first,Theoretical computer science,Nearest neighbor graph,Artificial intelligence,Nearest-neighbor chain algorithm,Hamming space,Nearest neighbor search,Hamming graph |
DocType | Citations | PageRank |
Conference | 2 | 0.39 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhansheng Jiang | 1 | 2 | 0.39 |
| Ling-Xi Xie | 2 | 429 | 37.79 |
| Xiaotie Deng | 3 | 3887 | 340.99 |
| Weiwei Xu | 4 | 875 | 50.19 |
| Jingdong Wang | 5 | 4198 | 156.76 |