.png){kind=icon}
返回
Trigraph Regularized Collective Matrix Tri-Factorization Framework on Multiview Features for Multilabel Image Annotation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2950701.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the explosive growth of image data, image annotation has been one of the most popular research directions in computer vision. It has been widely used in image retrieval, image analysis and understanding. Because traditional manual image annotation is time consuming, more advanced automatic annotation methods are needed. A major challenge in developing an efficient image annotation method is how to effectively use all available information contained in the data. To this end, this paper proposes a novel image annotation framework that uses multiple information from data. It employs nonnegative matrix tri-factorization (NMTF) to simultaneously factorize image-to-label, image-to-feature, and feature-to-label relation matrices using their intertype relationships and incorporates the intratype information through manifold regularizations. This method can be referred to as the trigraph regularized collective matrix tri-factorization framework (TG-CMTF). TG-CMTF captures the correlations among different labels, different images and different features. By taking advantage of these relations from images, features and labels, TG-CMTF can achieve better annotation performance than most state-of-the-art methods. The promising experimental results on three standard benchmarks have shown the effectiveness of this information. Furthermore, we show the annotation process as a precise optimization problem and solve it by an iterative algorithm, which proves the correctness of the proposed method from the mathematical theory.
Keyword:
Image annotation
Visualization
Semantics
Feature extraction
Correlation
Image retrieval
Task analysis
Image annotation
nonnegative matrix tri-factorization
manifold regularization
multilabel
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
The future of imaging: developing the tools for monitoring response to therapy in oncology: the 2009 Sir James MacKenzie Davidson Memorial lecture影像学的未来: 开发监测肿瘤学治疗反应的工具: 2009詹姆斯·麦肯齐·戴维森爵士纪念讲座
OAAI
OAAI分享收藏A novel Escherichia coli O157:H7 clone causing a major hemolytic uremic syndrome outbreak in China.一种新型大肠杆菌O157:H7克隆株，导致中国发生大规模溶血尿毒综合征暴发。.png)
IF0
PREAI
分享收藏
查看更多内容