L
Liyan Zhang
Researcher at University of California, Irvine
Publications - 6
Citations - 105
Liyan Zhang is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Cluster analysis & Identification (information). The author has an hindex of 5, co-authored 6 publications receiving 98 citations. Previous affiliations of Liyan Zhang include University of California & Nanjing University of Aeronautics and Astronautics.
Papers
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Proceedings ArticleDOI
A unified framework for context assisted face clustering
TL;DR: A unified framework that employs bootstrapping to automatically learn adaptive rules to integrate heterogeneous contextual information, along with facial features, together is proposed, which demonstrates the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.
Journal ArticleDOI
Context-based person identification framework for smart video surveillance
TL;DR: This work proposes a framework that leverages heterogeneous contextual information together with facial features to handle the problem of person identification for low-quality data and applies it to a real-world dataset consisting of several weeks of surveillance videos.
Journal ArticleDOI
Context-assisted face clustering framework with human-in-the-loop
TL;DR: A unified framework that employs bootstrapping to automatically learn adaptive rules to integrate heterogeneous contextual information, along with facial features, together is proposed, which demonstrates the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.
Proceedings ArticleDOI
Video entity resolution: Applying ER techniques for Smart Video Surveillance
TL;DR: This paper shows how the PI problem can be successfully resolved using a graph-based entity resolution framework called RelDC that leverages relationships among various entities for disambiguation and demonstrates the effectiveness and efficiency of the approach even with low quality video data.
Journal ArticleDOI
Query-Driven Approach to Face Clustering and Tagging
TL;DR: A query-driven approach to visual tagging, focusing on the application of face tagging and clustering, is introduced, which uses a data-driven Gaussian process model of facial appearance to write the probabilistic estimates of facial identity into a Probabilistic database, which can then support inference through query answering.