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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Book ChapterDOI
Classifying Objectionable Websites Based on Image Content
TL;DR: The system uses WIPEℳ (Wavelet Image Pornography Elimination) and statistics to provide robust classification of on-line objectionable World Wide Web sites, and has demonstrated 97% sensitivity and 97% specificity in classifying a Web site based solely on images.
Proceedings ArticleDOI
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
TL;DR: An annotation-driven image retrieval approach that is demonstrated to effectively handle cases of partially tagged and completely untagged image databases, multiple keyword queries, and example based queries with or without tags, all in near-realtime.
Journal ArticleDOI
Cybertools and archaeology
Dean R. Snow,Mark Gahegan,C. Lee Giles,Kenneth G. Hirth,George R. Milner,Prasenjit Mitra,James Z. Wang +6 more
TL;DR: In this paper, the authors present an anthropological study of the relationship between anthropology and geography, focusing on the use of anthropologists in the field of information sciences and technology at The Pennsylvania State University.
Journal ArticleDOI
Automatic image semantic interpretation using social action and tagging data
TL;DR: This study builds on an interdisciplinary confluence of insights from image processing, data mining, human computer interaction, and sociology to describe the folksonomic features of users, annotations and images.
Proceedings ArticleDOI
Searching Near-Replicas of Images via Clustering
TL;DR: RIME as mentioned in this paper uses a new clustering/hashing approach that first clusters similar images on adjacent disk cylinders and then builds indexes to access the clusters made in this way, which can detect images copies both more efficiently and effectively than the traditional content-based image retrieval systems that use tree-like structures to index images.