Jingen Liu

I am a Computer Scientist at SRI International, working on Computer Vision, Multimedia Processing, and Machine Learning. Before join SRI, I was a Research Fellow in the Department of EECS at University of Michigan, Ann Arbor, working with Profs. Benjamin Kuipers and Silvio Savarese. I got my Ph.D from University of Central Florida, under the supervision of Prof. Mubrak Shah.

Contact: liujg At cs.ucf.edu

 

[New] UCF YouTube action dataset with bounding boxing annotations is available.

PUBLICATIONS

Here is my Google Scholar Citations profile.
  • Jingen Liu, Qian Yu, Omar Javed, Saad Ali, Amir Tamrakar, Ajay Divakaran, Hui Cheng and Harpreet Sawhney, Video Event Recognition Using Concept Attributes, IEEE Workshops on Applications of Computer Vision (WACV), 2013. [PDF][Supp.material][Data] (Some interesting observations has been made on concept attribues based event recognition.)
  • Qian Yu, Jingen Liu, Hui Cheng, Ajay Divakaran, and Harpreet Sawhney, Multimedia Event Recounting with Concept Based Representation, ACM Multimedia 2012. [PDF]
  • Changhai Xu, Jingen Liu, and Benjamin Kuipers, Moving Object Segmentation Using Motor Signals, European Conference on Computer Vision (ECCV), 2012.[PDF][Video][Data][code]
  • Amir Tamrakar, Saad Ali, Qian Yu, Jingen Liu, Omar Javed, Ajay Divakaran, Hui Cheng, and Harpreet Sawhney, Evaluation of Low-level Features and Their Combinations for Complex Event Detection in Open Source Videos, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.[PDF]
  • Jingen Liu, Yang Yang, Imran Saleemi, and Mubarak Shah, Learning Semantic Features for Action Recognition via Diffusion Map, Computer Vision and Image Understanding, Volume 116, Issue 3, March 2012. [PDF]
  • Grace Tsai , Changhai Xu, Jingen Liu, and Benjamin Kuipers, Real-time Indoor Scene Understanding Using Bayesian Filtering with Motion Cues, IEEE International Conference on Computer Vision (ICCV), Spain, 2011.[PDF][poster][data]
  • Liang Mei, Jingen Liu, Alfred Hero and Silvio Savarese, Robust Object Pose Estimation via Statistical Manifold Modeling, IEEE International Conference on Computer Vision (ICCV), Spain, 2011. [PDF] [supp.material]
  • Jingen Liu, Mubarak Shah, Benjamin Kuipers, and Silvio Savarese, Cross-View Action Recognition via View Knowledge Transfer, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, 2011.(Oral) [PDF][slides]
  • Jingen Liu, Benjamin Kuipers, and Silvio Savarese, Recognizing Human Actions by Attributes, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, 2011.(Oral)[PDF][supp.material]
  • Changhai Xu, Jingen Liu, and Benjamin Kuipers, Motion Segmentation by Learning Homography Matrices from Motor Signals , Canadian Conference on Computer and Robot Vision (CRV), Newfoundland, Canada, 2011. (Oral)[PDF] (Winner of Best Student Paper Award)
  • Jingen Liu and Saad Ali, Learning Scene Semantics Using Fiedler Embedding, IEEE International Conference on Pattern Recognition (ICPR), Colorado Springs, 2010. [PDF]
  • Kishore Reddy, Jingen Liu and Mubarak Shah, Incremental Action Recognition Using Feature Tree, IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, 2009. [PDF][project page]
  • Yang Yang, Jingen Liu and Mubarak Shah, Video Scene Understanding with Multi-Scale Analysis, IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, 2009. [PDF][project page]
  • Jingen Liu, Yang Yang & Mubarak Shah, Learning Semantic Visual Vocabularies Using Diffusion Distance, IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), Miami, 2009. [PDF][project page]
  • Jingen Liu, Jiebo Luo & Mubarak Shah, Recognizing Realistic Actions from Videos "in the Wild", IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), Miami, 2009. (Oral)[PDF][Dataset (new: bounding box annotation is available)]
  • Jingen Liu, Jiebo Luo & Mubarak Shah, Action Recognition in Unconstrained Amateur Videos, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, 2009. (Oral)[PDF]
  • Naveed Imran, Jingen Liu, Jiebo Luo & Mubarak Shah, Event Recognition from Photo Collections via PageRank, ACM International Conference on Multimedia, 2009. (short paper)[PDF]
  • Fangshi Wang, Wei Lu, Jingen Liu & Mubarak Shah, Automatic Video Annotation with Adaptive Number of Key Words, IEEE International Conference on Pattern Recognition (ICPR), Tampa, 2008.
  • Jingen Liu & Mubarak Shah, Learning Human Actions via Information Maximization, IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), Alaska, 2008. [PDF][project page]
  • Jingen Liu, Saad Ali & Mubarak Shah, Recognizing Human Actions Using Multiple Features, IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), Alaska, 2008.[PDF][project page]
  • Jingen Liu & Mubarak Shah, Scene Modeling Using Co-Clustering , IEEE International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, 2007. [PDF][project page]
  • O. Bilal Orhan, Jingen Liu, et al., University of Central Florida at TRECVID 2008: Content Based Copy Detection and Surveillance Event Detection, TREC Video Retrieval Evaluation Workshop (TRECVID), Gaithersburg, November 2008. [PDF]
  • Yun Zhai, Jingen Liu & Mubarak Shah, Automatic Query Expansion in News Video Retrieval, IEEE International Conference on Multimedia and Expo (ICME), Toronto, Canada, 2006.[PDF]
  • Andrew Miller, Aslan Basharat, B. White, Jingen Liu & Mubarak Shah, Person and Vehicle Tracking in Surveillance Video. CLEAR Evaluation Campaign and Workshop, Baltimore , 2007. [PDF]
  • Jingen Liu , Yusuf Aytar, Bilal Orhan, Jenny Han & Mubarak Shah, University of Central Florida at TRECVID 2007: Semantic Video Classification and Automatic Search, TREC Video Retrieval Evaluation Workshop (TRECVID), Gaithersburg, 2007. [PDF]
  • Jingen Liu , Yun Zhai & Mubarak Shah, PEGASUS: An Information Mining System for TV News Videos, SPIE Defense and Security Symposium, Orlando, 2006. [PDF]
  • Jingen Liu , Yun Zhai, Arslan Basharat, Bilal Orhan, Saad Khan, Humera Noor, Phillip Berkowitz and Mubarak Shah, University of Central Florida at TRECVID 2006: High-Level Feature Extraction and Video Search, TREC Video Retrieval Evaluation Workshop (TRECVID), Gaithersburg, 2006. [PDF]
  • Yun Zhai, Jingen Liu, Xiaochun Cao, Arslan Basharat, Asaad Hakeem, Saad Ali, Mubarak Shah, Costantino Grana & Rita Cucchiarra, Video Understanding and Content-Based Retrieval, TREC Video Retrieval Evaluation Workshop (TRECVID), Gaithersburg, Maryland, 2005. [PDF]

RESEARCH PROJECTS

Learning Semantic Vocabularies using Diffusion Distance

We use diffusion maps to automatically learn a semantic vocabulary from abundant quantized mid-level features. Each mid-level feature is represented by the vector of point-wise mutual information (PMI). In this mid-level feature space, we believe the features produced by similar sources must lie on a certain manifold. To capture the intrinsic geometric relations between features, we measure their dissimilarity using diffusion distance. We have applied this technique to action recognition, video scene understanding (activity discovery), crowd tracking.... [project page]


CLEAR Evaluation Campaign , CHIL&NIST

I worked as an active member of the UCF CLEAR evaluation team during 2007, on tasks as people and vehicle tracking in surveillance video. We applied our KNIGHT system to detect and track the moving objects. Classfiers are used to recognize the object. My major contribution in this evaluation is designing effective people and vehicle classifiers on the detected objects [report 2007].


Learning Realistic Actions from Unconstrained Videos

We developed a systematic framework for recognizing realistic actions from unconstrained videos which have tremendous variations due to camera motion, background clutter, changes in object appearance and scale, etc. We use both motion and static features from the videos. Since the raw features of both types are dense yet noisy, we propose strategies to prune these features...... [project page]


Action Recognition Using Bag of Video-Words

Developed an algorithm that used mutual information between the cuboids (3D interest points) and videos to automatically discover the optimized number of video-word-cluster while balancing the discrimination and compactness of the model. The human actions are modeled by the video-word-clusters. Our approach is able to handle camera motion, changes of illumination and appearance, and zooming etc. [project page]


Action Recognition Using Multiple Features

We extracted spatiotemporal features (cuboids) and Spin-Image features (which is generated from the Space-time volume) from the action object. A general framework based on Fiedler Embedding was developed to fuse the two complementary features for human action recognition.[project page]


Scene Understanding and Classification

Beyond bag of features, there exists some hidden relationship between the low level image patches. Those hidden relationships are called semantic intermediate concepts. We utilize the Maximization of Mutual Information co-clustering approach to automatically discover the intermediate concepts. The scene recognition is conducted by exploring the statistic information of the intermediate concepts [project page].

 


TREC Video Retrieval Evaluation, NIST

I worked as an active member of the UCF T RECVID team during 200 5 and team leader during TRECVID 200 6 and 2007. The tasks which I was involved in are high-level feature extraction and video retrieval . During 2006, our performance on high-level feature task is top 5 of 35 teams, and for interactive search our run ranks top 4 of 36 total runs from the community. During 2007, our performances on high level feature and automatic search task were above the median.[report 2005, report2006, report2007]


Features Fusion through Fiedler Embedding

Developed a principled framework for fusing information from complementary features (patches, contours, segments) for object recognition [project page].


Image Classification Using Boosted Hidden Features

We automatically extracted hidden features from multiple low level features including spatial color statistics, spatial edge histogram, and SIFT features, which contains both the local and global information of the image. In stead of assigning equal weights to the hidden features, AdaBoost creates the classifier by automatically select the prominent features for each category.


PEGASUS: A Content-Based Video Retrieval System

We created the PEGASUS which is an interactive video retrieval system. It is designed for retrieving relevant video shots from the broadcast news programs given the target topics. It contains three components: web-based user interface, server and fast feature index system. Both the speech transcript feature and visual features have been explored.System web http://pegasus.cs.ucf.edu:8080.[project page]


Consumer Video Indexing, Kodak

In this project we worked on developing a system for consumer video indexing. We explored two approaches for two sorts of videos, e.g. physical scene videos and action videos. For scene videos, we detected video shot boundaries and indexed the shots by the key frames of the shot. For action videos, we explored the kinematic features of the optical flows of the action video, and videos are indexed by these invariant flow features


 

Coordination Computing (under supervision of Dr. Marinescu)

We worked on developing a coordination system based on Condensed Graph and Petri Net for distributed task scheduling. We set up the “UCF Summer Grid” as our experiment platform using the NFS Initiative Middleware .

LINKS