...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >Semi-supervised dual low-rank feature mapping for multi-label image annotation
【24h】

Semi-supervised dual low-rank feature mapping for multi-label image annotation

机译：用于多标签图像注释的半监控双低级功能映射

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic image annotation as a typical multi-label learning problem, has gained extensive attention in recent years owing to its application in image semantic understanding and relevant disciplines. Nevertheless, existing annotation methods share the same challenge that labels annotated on the training images are usually incomplete and unclean, while the need for adequate training data is costly and unrealistic. Being aware of this, we propose a dual low-rank regularized multi-label learning model under a graph regularized semi-supervised learning framework, which can effectively capture the label correlations in the learned feature space, and enforce the label matrix be self-recovered in label space as well. To be specific, the proposed approach firstly puts forward a label matrix refinement approach, by introducing a label coefficient matrix to build a linear self-recovery model. Then, graph Laplacian regularization is introduced to make use of a large number of unlabeled images by enforcing the local geometric structure on both labeled and unlabeled images. Lastly, we exploit dual trace norm regularization on both feature mapping matrix and self-recovery coefficient matrix to capture the correlations among different labels in both feature space and label space, and control the model complexity as well. Empirical studies on four real-world image datasets demonstrate the effectiveness and efficiency of the proposed framework.
机译：自动图像注释作为典型的多标签学习问题，近年来由于其在图像语义理解和相关学科的应用而获得了广泛的关注。然而，现有的注释方法共享相同的挑战，即在培训图像上注释的标签通常不完整和不洁净，而对充足的培训数据的需求是昂贵的和不切实际的。要了解这一点，我们在正规化的半监督学习框架下提出了一个双低级正则化多标签学习模型，它可以有效地捕获学习特征空间中的标签相关性，并强制实施标签矩阵自恢复在标签空间也是如此。具体而言，所提出的方法首先通过引入标签系数矩阵来构建线性自恢复模型来提出标签矩阵细化方法。然后，引入了图表LAPLACIAN规范化通过在标记和未标记的图像上强制执行本地几何结构来利用大量未标记的图像。最后，我们在特征映射矩阵和自恢复系数矩阵上利用双跟踪规范正则化，以捕获特征空间和标签空间中不同标签之间的相关性，并控制模型复杂性。四个现实世界图像数据集的实证研究证明了所提出的框架的有效性和效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号