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 ArticleDOI
Image retrieval: Ideas, influences, and trends of the new age
TL;DR: Almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation are surveyed, and the spawning of related subfields are discussed, to discuss the adaptation of existing image retrieval techniques to build systems that can be useful in the real world.
Journal ArticleDOI
SIMPLIcity: semantics-sensitive integrated matching for picture libraries
TL;DR: SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation to improve retrieval.
Book ChapterDOI
SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries
TL;DR: The SIMPLIcity system represents an image by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location, which classifies images into categories intended to distinguish semantically meaningful differences.
Journal ArticleDOI
Automatic Linguistic Indexing of Pictures by a statistical modeling approach
Jia Li,James Z. Wang +1 more
TL;DR: This paper implemented and tested the ALIP (Automatic Linguistic Indexing of Pictures) system on a photographic image database of 600 different concepts, each with about 40 training images and demonstrated the good accuracy of the system and its high potential in linguistic indexing of photographic images.
Book ChapterDOI
Studying aesthetics in photographic images using a computational approach
TL;DR: This paper treats the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source and extracts certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images.