Dense Trajectories and DHOG for Classification of Viewpoints from Echocardiogram Videos.
Dense Trajectories and DHOG for Classification of Viewpoints from Echocardiogram Videos.
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用于超声心动图视频观点分类的密集轨迹和 DHOG
DOI：
10.1155/2016/9610192
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发表时间：
2016
影响因子：
--
通讯作者：
Li W
中科院分区：
文献类型：
--
作者：
Huang L;Zhang X;Li W
在超声心动图临床计算机辅助诊断中，一个重要的步骤是对不同角度、不同区域的超声心动图视频进行自动分类。提出了一种基于稠密轨迹和梯度方向差分直方图的超声心动图视频分类算法。首先，我们使用稠密网格方法来描述超声心动图序列的每一帧中的特征点，然后应用稠密光流跟踪这些特征点。为了克服超声心动图视频快速不规则运动的影响，得到更鲁棒的跟踪结果，我们还设计了一种轨迹描述算法，该算法利用光流的导数来获得运动轨迹信息，并将不同的特征（例如，轨迹形状、DHOG、霍夫和MBH）与时空金字塔的嵌入式结构信息。为了避免“维数灾难”，我们使用Fisher向量来降低特征描述的维数，然后使用SVM线性分类器来提高最终的分类结果。超声心动图视频分类的平均准确率为77.12%的所有八个观点和100%的三个主要观点。
In echo-cardiac clinical computer-aided diagnosis, an important step is to automatically classify echocardiography videos from different angles and different regions. We propose a kind of echocardiography video classification algorithm based on the dense trajectory and difference histograms of oriented gradients (DHOG). First, we use the dense grid method to describe feature characteristics in each frame of echocardiography sequence and then track these feature points by applying the dense optical flow. In order to overcome the influence of the rapid and irregular movement of echocardiography videos and get more robust tracking results, we also design a trajectory description algorithm which uses the derivative of the optical flow to obtain the motion trajectory information and associates the different characteristics (e.g., the trajectory shape, DHOG, HOF, and MBH) with embedded structural information of the spatiotemporal pyramid. To avoid “dimension disaster,” we apply Fisher's vector to reduce the dimension of feature description followed by the SVM linear classifier to improve the final classification result. The average accuracy of echocardiography video classification is 77.12% for all eight viewpoints and 100% for three primary viewpoints.