J
James Z. Wang
Researcher at Pennsylvania State University
Publications - 234
Citations - 23185
James Z. Wang is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Image retrieval & Automatic image annotation. The author has an hindex of 57, co-authored 225 publications receiving 21890 citations. Previous affiliations of James Z. Wang include Penn State College of Information Sciences and Technology & University of Minnesota.
Papers
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Journal Article
The SIR-B Observations of Microwave Backscatter Dependence on Soil Moisture, Surface Roughness, and
TL;DR: In this paper, an L-band synthetic-aperture perture radar was used to study the microwave backscatter dependence on soil moisture, surface roughness, and vegetation cover.
Proceedings ArticleDOI
ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics
Ritendra Datta,James Z. Wang +1 more
TL;DR: ACQUINE - Aesthetic Quality Inference Engine, a publicly accessible system which allows users to upload their photographs and have them rated automatically for aesthetic quality, is presented, a significant first step in recognizing human emotional reaction to visual stimulus.
Journal ArticleDOI
POLYGALACTURONASE INVOLVED IN EXPANSION3 Functions in Seedling Development, Rosette Growth, and Stomatal Dynamics in Arabidopsis thaliana.
Yue Rui,Chaowen Xiao,Hojae Yi,Baris Kandemir,James Z. Wang,Virendra M. Puri,Charles T. Anderson +6 more
TL;DR: It is demonstrated that PGX3-mediated pectin degradation affects stomatal development in cotyledons, promotes rosette expansion, and modulates guard cell mechanics in adult plants.
Proceedings ArticleDOI
The story picturing engine: finding elite images to illustrate a story using mutual reinforcement
TL;DR: A human behavior model based on a discrete state Markov process which captures the intuition for the technique is presented and experimental results have demonstrated the effectiveness of the scheme.
Book
Integrated Region-Based Image Retrieval
TL;DR: Integrated Region-Based Image Retrieval presents a wavelet-based approach for feature extraction, combined with integrated region matching, and demonstrates an experimental image retrieval system called Simplicity (Semantics-sensitive Integrated Matching for Picture Libraries).