所有成员 - 研究人员

Jinqiao Wang, 王金桥 - 副研究员


Jinqiao Wang, Assistant Professor. He received his B.E in Mechatronic Engineering from Hebei University of Technology in 2001, M.S degree in Mechatronic Engineering from Tianjin University in 2004. He received the Ph.D degree in National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences in 2008. Since July 2008, He has joined NLPR as Assistant professor. His research interests include multimedia information retrieval, mobile multimedia analysis, object detection, tracking and classification.

Phone: +86 010 6252 7720
Fax: +86 010 6255 1993
Email: jqwang@nlpr.ia.ac.cn
Address: Room 1112, No.95 Zhongguancun East Road, Haidian Dist., Beijing, China. 100080

Research Interests

Research Experiences

Selected Publications [Full Publication Lists]

NO Articles
[1]Jinqiao Wang, Lingyu Duan, Qingshan Liu, Hanqing Lu, and Jesse S. Jin, A Multi-model Segmentation and Representation Scheme for Broadcast Video, IEEE Transactions on Multimedia, April 2008.
[2]Jinqiao Wang, Yikai Fang and Hanqing Lu, Online Video Advertising Based on User's Attention Relevancy Computing, ICME 2008.
[3]Jinqiao Wang, Lingyu Duan, Lei Xu, Hanqing Lu and Jesse S. Jin, TV Ad Video Categorization with Probabilistic Latent Concept Learning, ACM MIR'07, Sep. 22, 2007.
[4]Jinqiao Wang,Lingyu Duan, Hanqing Lu, Jesse S. Jin, and Changsheng Xu. A Mid-Level Scene Change Representation Via Audiovisual Alignment, ICASSP'06. Toulouse, France, May, 2006.
[5]Jinqiao Wang, Lingyu Duan, Hanqing Lu and Jesse S. Jin, A Semantic Image Category for Structuring TV Broadcast Video Streams, PCM'06. Hangzhou, China, 2006.

Demos

1. FMPI feature extraction program. [Zip] Frame Marked with Production Information(FMPI), POIM(Program Oriented Information Frame) is an extension of FMPI in TV broadcast streams. usage:

FMPI.exe imagename imagetype

The input image format can be JPG(ImageType=1), BMP(ImageType=2), TIF(ImageType=3) and PGM(ImageType=4). The filename of output feature is imagename.fea. The current version only support color images. The totol dimension of feature is 141, including color, texture, and edge features. The code can only be used for purpose of research, however please acknowledge its use with a citation PCM06 or MM06. If you feel interest for the sourcecode, email me jqwang@nlpr.ia.ac.cn.


2. Context Saliency based Image Summarization (Submitted to ICME09P).[Experimental Results]

Abstract:

The problem of image summarization is to determine a smaller representation but faithfully represent the original visual image appearance. In this paper, we propose a context saliency based image summarization approach in a supervised manner. Since merely visual saliency as importance measure is not enough, we incorporate redundancy-based contrast analysis and geometric segmentation into context saliency through naive Bayesian inference. Then we introduce a grid-based piecewise linear image warping scaleplate to maintain the proportion of salient objects. We argue that the image summaries should be appraised with target device specification under perception constrains, and we adopt the sweet spot evaluation to generate a flexible model that automatically combines cropping and warping methods. Additionally, we explore potential extension on multiple applications such as video retargeting, digital matting, image browsing etc. Experimental results show comparable performance compared to the state-of-art on common data sets.