Gaze prediction for first-person videos based on inverse non-negative sparse coding with determinant sparse measure
Gaze prediction for first-person videos based on inverse non-negative sparse coding with determinant sparse measure
复制标题
基于行列式稀疏测度逆向非负稀疏编码的第一人称视频注视预测
DOI：
10.1016/j.jvcir.2021.103367
复制
发表时间：
2021-11-12
影响因子：
2.6
通讯作者：
Ding, Shuxue
中科院分区：
文献类型：
--
作者：
Li, Yujie;Tan, Benying;Ding, Shuxue
注视预测是处理大量视频输入视觉信息的一种重要方法。最近的注视预测算法通常采用稀疏模型，并假设视频帧中的每个超像素都可以表示为几个显著超像素的线性组合。然而，由于视频信号包含非否定请求的知识不足，它们没有足够的驱动。因此，我们开发了一种基于具有行列式稀疏度量的逆稀疏编码框架的凝视预测方法。通过引入这种稀疏度量，解是非负的，并且比传统的稀疏约束更稀疏。然而，所提出的优化问题变得非凸，难以求解。为了有效地解决相应的非凸优化问题，我们提出了一种基于凸函数规划的差分算法，该算法可以产生全局解。实验结果表明，与现有算法相比，该方法的精度有所提高。
Gaze prediction is a significant approach for processing a large amount of incoming visual information of videos. Recent gaze prediction algorithms often employ sparse models with the assumption that every superpixel in the video frames can be represented as linear combinations of a few salient superpixels. However, they are not actuated enough because of the insufficient knowledge that video signals contain a non-negative request. Hence, we develop a novel gaze prediction based on an inverse sparse coding framework with a determinant sparse measure. By introducing this sparse measure, the solutions are non-negative and sparser than conventional sparse constraints. However, the proposed optimization problem becomes nonconvex, which is difficult to solve. To efficiently address the corresponding nonconvex optimization problem, we propose a novel algorithm based on the difference in convex function programming, which can yield the global solutions. Experimental results indicate the improved accuracy of the proposed approach compared with state-of-the-art algorithms.