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.
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Original articles Content-based image indexing and searching using Daubechies' wavelets
TL;DR: WBIIS as mentioned in this paper applies a Daubechies' wavelet transform for each of the three opponent color components, and the wavelet coefficients in the lowest few frequency bands, and their variances, are stored as feature vectors.
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Aesthetics and Emotions in Images
Dhiraj Joshi,Ritendra Datta,Elena A. Fedorovskaya,Quang-Tuan Luong,James Z. Wang,Jia Li,Jiebo Luo +6 more
TL;DR: This tutorial defines and discusses key aspects of the problem of computational inference of aesthetics and emotion from images and describes data sets available for performing assessment and outline several real-world applications where research in this domain can be employed.
Journal ArticleDOI
Content-based image indexing and searching using Daubechies' wavelets
TL;DR: WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases, which performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms.
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Image processing for artist identification
Chris R. Johnson,Ella Hendriks,Igor Berezhnoy,Eugene Brevdo,Shannon M. Hughes,Ingrid Daubechies,Jia Li,Eric O. Postma,James Z. Wang +8 more
TL;DR: The approaches to brushwork analysis and artist identification developed by three research groups are described within the framework of this data set of 101 high-resolution gray-scale scans of paintings within the Van Gogh and Kroller-Muller museums.
Proceedings ArticleDOI
Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation
TL;DR: The proposed deep multi-patch aggregation network integrates shared feature learning and aggregation function learning into a unified framework and significantly outperformed the state of the art in all three applications.