Semi-supervised multi-graph hashing for scalable similarity search
文献类型：期刊论文
| 作者 | Cheng, Jian1 ; Leng, Cong1; Li, Peng1; Wang, Meng2; Lu, Hanging1; Lu, Hanqing; Jian Cheng
|
| 刊名 | COMPUTER VISION AND IMAGE UNDERSTANDING
 |
| 出版日期 | 2014-07-01 |
| 卷号 | 124页码:12-21 |
| 关键词 | Hashing Multiple graph learning Multiple modality Semi-supervised learning |
| 英文摘要 | Due to the explosive growth of the multimedia contents in recent years, scalable similarity search has attracted considerable attention in many large-scale multimedia applications. Among the different similarity search approaches, hashing based approximate nearest neighbor (ANN) search has become very popular owing to its computational and storage efficiency. However, most of the existing hashing methods usually adopt a single modality or integrate multiple modalities simply without exploiting the effect of different features. To address the problem of learning compact hashing codes with multiple modality, we propose a semi-supervised Multi-Graph Hashing (MGH) framework in this paper. Different from the traditional methods, our approach can effectively integrate the multiple modalities with optimized weights in a multi-graph learning scheme. In this way, the effects of different modalities can be adaptively modulated. Besides, semi-supervised information is also incorporated into the unified framework and a sequential learning scheme is adopted to learn complementary hash functions. The proposed framework enables direct and fast handling for the query examples. Thus, the binary codes, learned by our approach can be more effective for fast similarity search. Extensive experiments are conducted on two large public datasets to evaluate the performance of our approach and the results demonstrate that the proposed approach achieves promising results compared to the state-of-the-art methods. (C) 2014 Elsevier Inc. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000337663600003 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3334]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Jian Cheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Jian,Leng, Cong,Li, Peng,et al. Semi-supervised multi-graph hashing for scalable similarity search[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2014,124:12-21. |
| APA | Cheng, Jian.,Leng, Cong.,Li, Peng.,Wang, Meng.,Lu, Hanging.,...&Jian Cheng.(2014).Semi-supervised multi-graph hashing for scalable similarity search.COMPUTER VISION AND IMAGE UNDERSTANDING,124,12-21. |
| MLA | Cheng, Jian,et al."Semi-supervised multi-graph hashing for scalable similarity search".COMPUTER VISION AND IMAGE UNDERSTANDING 124(2014):12-21. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。