We proposed a general framework which applies classifiers with different complexity to discriminate segments in an image.
Our unsupervised hierarchical segmentation results achieve similar or better performance in several standard benchmarks compared with the current state-of-the-art methods based on learning, and has been accepted to ACCV 2014. (Paper, Poster)
We proposed an efficient algorithm which decomposes the unsupervised Multiple Foreground Co-segmentation problem into three sub-problems: segmentation, matching and figure-ground classification.
Our method improves the accuracy of the state-of-the-art method by 13% in a standard benchmark, and has been accepted by CVIU (Paper).
We formulated our objective function at the superpixel level rather than the pixel level as the traditional optical flow method did.
We applied dictionary learning, visual and motion saliency to extract the foreground object from videos. This research has been accepted to TIP (Paper).
We extract answering logs of the exercises from Junyi Academy (http://www.junyiacademy.org/), an E-learning website similar to Khan Academy.
We use crowdsourcing and machine learning to discover relationships between exercises. Based on that, we will design a mechanism of adaptive test to improve learning experiences of Junyi academy. (Paper, Presentation, Demo, Dataset)
Student Modeling and Prerequisite Verification in Knowledge Tree
Computer Vision and Multimedia
In a single surveillance video, we incorporate tracking data into the photo pop-up algorithm, and utilize RANSAC (random sample consensus) to reduce around 80% of tracking noise (PPT & Videos).
Selected Course Projects
We use Bayesian learning to model the non-linear relationships between quality of experience (QoE) and multiple factors.
Our experiment shows that active sampling can be used to reduce the number of samples collected from crowdsourcing for building such model.