Name
Abstract | ||
|---|---|---|
Binary embedding is an effective way for nearest neighbor (NN) search as binary code is storage efficient and fast to compute. It tries to convert real-value signatures into binary codes while preserving similarity of the original data, and most binary embedding methods quantize each projected dimension to one bit (presented as 0/1). As a consequence, it greatly decreases the discriminability of original signatures. In this paper, we first propose a novel quantization strategy triple-bit quantization (TBQ) to solve the problem by assigning 3-bit to each dimension. Then, asymmetric distance (AD) algorithm is applied to re-rank candidates obtained from hamming space for the final nearest neighbors. For simplicity, we call the framework triple-bit quantization with asymmetric distance (TBAD). The inherence of TBAD is combining the best of binary codes and real-value signatures to get nearest neighbors quickly and concisely. Moreover, TBAD is applicable to a wide variety of embedding techniques. Experimental comparisons on BIGANN set show that the proposed method can achieve remarkable improvement in query accuracy compared to original binary embedding methods. |
Year | Venue | Field |
|---|---|---|
2016 | PCM | k-nearest neighbors algorithm,Fixed-radius near neighbors,Pattern recognition,Best bin first,Computer science,Ball tree,Binary code,Algorithm,Nearest neighbor graph,Artificial intelligence,Hamming space,Nearest neighbor search |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Deng | 1 | 12 | 11.04 |
| Hongtao Xie | 2 | 439 | 47.79 |
| Wei Ma | 3 | 3 | 1.04 |
| Qionghai Dai | 4 | 3904 | 215.66 |
| Jianjun Chen | 5 | 39 | 12.52 |
| Ming Lu | 6 | 2 | 2.73 |