Image distance metric learning based on neighborhood sets for automatic image annotation |
| |
| Affiliation: | 1. School of Computer, Central China Normal University, Wuhan 430079, PR China;2. Département de Physique, École Normale Supérieure, 24, rue Lhomond, 75231 Paris Cedex 5, France;1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China;2. Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States;3. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;2. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;3. SYSU-CMU Shunde International Joint Research Institute, Shunde, Guangdong, China;4. Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510006, China;1. Faculty of Arts and Science, Kyushu University, 819-0395, Japan;2. Faculty of Information Science and Electrical Engineering, Kyushu University, Japan;1. State Key Lab of CAD&CG, Zhejiang University, China;2. Software School of Xiamen University, China |
| |
| Abstract: | Since there is semantic gap between low-level visual features and high-level image semantic, the performance of many existing content-based image annotation algorithms is not satisfactory. In order to bridge the gap and improve the image annotation performance, a novel automatic image annotation (AIA) approach using neighborhood set (NS) based on image distance metric learning (IDML) algorithm is proposed in this paper. According to IDML, we can easily obtain the neighborhood set of each image since obtained image distance can effectively measure the distance between images for AIA task. By introducing NS, the proposed AIA approach can predict all possible labels of the image without caption. The experimental results confirm that the introduction of NS based on IDML can improve the efficiency of AIA approaches and achieve better annotation performance than the existing AIA approaches. |
| |
| Keywords: | Automatic image annotation Improve performance Image distance metric learning Neighborhood sets Algorithm performance Visual similarity Semantic similarity Probability density ratio |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
