1
Jia Li, James Z Wang: Real-time computerized annotation of pictures. The Penn State Research Foundation, Gifford Krass Sprinkle Anderson & Citkowski P C, Douglas L Wathen, May 10, 2011: US07941009 (84 worldwide citation)

A computerized annotation method achieves real-time operation and better optimization properties while preserving the architectural advantages of the generative modeling approach. A novel clustering algorithm for objects is represented by discrete distributions, or bags of weighted vectors, thereby ...


2
Li Jia, Wang James Z: Real-time computerized annotation of pictures. The Penn State Research Foundation, Li Jia, Wang James Z, WATHEN Douglas L, September 4, 2008: WO/2008/105962 (1 worldwide citation)

A computerized annotation method achieves real-time operation and better optimization properties while preserving the architectural advantages of the generative modeling approach. A novel clustering algorithm for objects is represented by discrete distributions, or bags of weighted vectors, thereby ...


3
James Z Wang, Neela Sawant, Jia Li: Instance-weighted mixture modeling to enhance training collections for image annotation. The Penn State Research Foundation, Dinsmore & Shohl, May 9, 2017: US09646226

Automatic selection of training images is enhanced using an instance-weighted mixture modeling framework called ARTEMIS. An optimization algorithm is derived that in addition to mixture parameter estimation learns instance-weights, essentially adapting to the noise associated with each example. The ...


4
James Z Wang, Yu Zhang, Jia Li: Massive clustering of discrete distributions. The Penn State Research Foundation, Dinsmore & Shohl, August 1, 2017: US09720998

The trend of analyzing big data in artificial intelligence requires more scalable machine learning algorithms, among which clustering is a fundamental and arguably the most widely applied method. To extend the applications of regular vector-based clustering algorithms, the Discrete Distribution (D2) ...


5
Jia Li, James Z Wang: Real-time computerized annotation of pictures. The Penn State Research Foundation, Gifford Krass Sprinkle Anderson & Citkowski PC, August 13, 2009: US20090204637-A1

A computerized annotation method achieves real-time operation and better optimization properties while preserving the architectural advantages of the generative modeling approach. A novel clustering algorithm for objects is represented by discrete distributions, or bags of weighted vectors, thereby ...