Today, the number of available videos on the Internet is significantly increased. Content-
based video retrieval is used for finding the users' desired items among these big video …

Extracting discriminative and robust features from video sequences is the first and most
critical step in human action recognition. In this paper, instead of using handcrafted features …

We present a novel descriptor, called spatio-temporal Laplacian pyramid coding (STLPC),
for holistic representation of human actions. In contrast to sparse representations based on …

To improve both the efficiency and accuracy of video semantic recognition, we can perform
feature selection on the extracted video features to select a subset of features from the high …

In this paper, we build a general summarization framework for both of edited video and raw
video summarization. Overall, our work can be divided into three folds. 1) Four models are …

Direct estimation of cardiac ventricular volumes has become increasingly popular and
important in cardiac function analysis due to its effectiveness and efficiency by avoiding an …

Learning deep representations have been applied in action recognition widely. However,
there have been a few investigations on how to utilize the structural manifold information …

With the development of depth sensors, low latency 3D human action recognition has
become increasingly important in various interaction systems, where response with minimal …

This study proposes a novel human action recognition method using regularized multi-task
learning. First, we propose the part Bag-of-Words (PBoW) representation that completely …

This paper proposes a unified single/multi-view human action recognition method via
regularized multi-task learning. First, we propose the pyramid partwise bag of words …