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“Visual Data Analytics for Dietary Assessment and Video Compression” by Dr. Fengqing Maggie Zhu

Date: January 29th, 2020

Visual Data Analytics for Dietary Assessment and Video Compression

Fengqing Maggie Zhu

Assistant Professor, School of Electrical and Computer Engineering, Purdue University

January 29 @ 6:30 pm – 8:30 pm PST

Event Sponsored and Organized By:

IEEE Signal Processing Society, Chapter of Santa Clara Valley


IEEE Information Theory Society, SCV Chapter

IEEE Circuit and Systems Society, SCV Chapter

Registration: Here.


6:30pm-7:00pm: Registration, Food, Networking

7:00pm-8:00pm: Talk

8:00pm-8:30pm: Q&A and Networking


Santa Clara University, California Misson Room in Benson Memorial Center820 Alviso Street, Santa Clara, CA, 95050

Parking information will be posted shortly.


One of the most important behavior both socially and culturally that impacts one’s health is diet. Assessing dietary intake of children and adults with high accuracy remains a major challenge in

the field of nutrition and health. In this talk, I will present the design and development of a novel food record using a mobile device to provide an accurate measure of daily food and nutrient intake. Different computer vision and machine learning methods have been developed to automatically identify foods and estimate energy from a meal image. These methods have been validated on both public datasets and our own datasets collected from dietary studies. Significant advances in video coding has been developed in the last two decades to satisfy the growing requirements of video applications. In this talk, we introduce the design and development of a switchable region-based coding scheme that leverages semantic segmentation to achieve a superior coding efficiency. We also present a novel perceptual quality assessment measure for the region-based coding scheme since point-by-point metrics are inadequate. The proposed method is evaluated on both standard test sets and the YouTube UGC datasets, which shows significant data rate reduction with satisfying visual quality verified by a subjective study.


Fengqing Maggie Zhu is an Assistant Professor of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. Dr. Zhu received the B.S.E.E. (with highest distinction), M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University in 2004, 2006 and 2011, respectively. Her research interests include image processing and analysis, video compression and computer vision. Prior to joining Purdue in 2015, she was a Staff Researcher at Futurewei Technologies (USA), where she received a Certification of Recognition for Core Technology Contribution in 2012. She is the recipient of an NSF CISE Research Initiation Initiative (CRII) award in 2017, and a Google Faculty Research Award in 2019.

  • January 2020
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