Name
Abstract | ||
|---|---|---|
Rate-distortion optimization has been a successful and significant method in video coding. By introducing Lagrange multiplier optimization into compress procedure, we can choose coding parameters simply and effectively. In nearest neighbor search problem, hashing has been a popular method to reduce computation and storage cost, which is consistent with video coding method. Conventionally, we evaluate a hashing method with mAP (mean average precision) w.r.t. different bit number, but leave bit cost as an independent measure index. In this paper, we make an attempt to combine retrieval accuracy and bit cost to make evaluation more comprehensive, using the concept of rate distortion optimization. Consequently, we obtain an evaluation criterion to judge which work point of a specific hashing method is better, taking both the accuracy and the bit cost into account. The exertion of an algorithm can be then determined. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Performance evaluation, Rate distortion theory, Information retrieval, nearest neighbor search |
Field | DocType | ISSN |
Pattern recognition,Lagrange multiplier,Computer science,Algorithm,Artificial intelligence,Hash function,Bit numbering,Distortion,Rate–distortion theory,Nearest neighbor search,Rate–distortion optimization,Binary number | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiding Liu | 1 | 0 | 0.34 |
| Wengang Zhou | 2 | 22 | 12.93 |
| Houqiang Li | 3 | 2090 | 172.30 |