Multimedia, Search engine indexing, Speech recognition, User profile and Content are his primary areas of study. His study looks at the intersection of Multimedia and topics like Presentation with Selection and Natural language. His Search engine indexing study necessitates a more in-depth grasp of Information retrieval.
His Information retrieval research includes elements of Construct and Table. His Speech recognition study integrates concerns from other disciplines, such as Artificial neural network and Feature extraction. He works mostly in the field of User profile, limiting it down to concerns involving Media content and, occasionally, CLIPS, Persistent data structure and Identification.
Zhu Liu spends much of his time researching Multimedia, Artificial intelligence, Content, Information retrieval and Media content. The various areas that Zhu Liu examines in his Multimedia study include Video capture, Identification, World Wide Web, Presentation and CLIPS. His work in the fields of Artificial intelligence, such as Image segmentation, intersects with other areas such as TRECVID.
His Image segmentation research focuses on subjects like Contextual image classification, which are linked to Speech recognition, Classifier, Audio signal and Feature extraction. His Information retrieval research is mostly focused on the topic Search engine indexing. His Media content research incorporates elements of Database and Subject.
The scientist’s investigation covers issues in Multimedia, Media content, Human–computer interaction, Action and Content. His studies deal with areas such as Timestamp, Upload and CLIPS as well as Multimedia. His biological study spans a wide range of topics, including New media, Metadata and Media consumption.
The Human–computer interaction study combines topics in areas such as Object, Virtual image and Videoconferencing. His study with Object involves better knowledge in Artificial intelligence. Content combines with fields such as User equipment, User feedback, Media channel, Service and Set in his work.
His primary areas of investigation include Human–computer interaction, Multimedia, Monitoring food intake, Environmental health and Context based. Zhu Liu interconnects Representation, Session, Visual communication and Communications system in the investigation of issues within Human–computer interaction. His research in Multimedia intersects with topics in Upload, Focus and CLIPS.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Multimedia content analysis-using both audio and visual clues
Yao Wang;Zhu Liu;Jin-Cheng Huang.
IEEE Signal Processing Magazine (2000)
Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Zhu Liu;Yao Wang;Tsuhan Chen.
multimedia signal processing (1998)
Method and apparatus for interactively retrieving content related to previous query results
Lee Begeja;David Crawford Gibbon;Zhu Liu;Bernard S. Renger.
Library of existing spoken dialog data for use in generating new natural language spoken dialog systems
Lee Begeja;Giuseppe Di Fabbrizio;David Crawford Gibbon;Dilek Z. Hakkani-Tur.
System and method for selecting a multimedia presentation to accompany text
Zhu Liu;Andrea Basso;Lee Begeja;David C. Gibbon.
System and method for automated multimedia content indexing and retrieval
David Crawford Gibbon;Qian Huang;Zhu Liu;Aaron Edward Rosenberg.
System and method for playing media
Andrea Basso;Zhu Liu;Bernard S. Renger;Behzad Shahraray.
System and method for adaptive content rendition
Andrea Basso;David C. Gibbon;Zhu Liu;Bernard S. Renger.
Alternate delivery mechanisms of customized video streaming content to devices not meant for receiving video
Lee Begeja;David Gibbon;Zhu Liu;Robert Markowitz.
Audio feature extraction and analysis for scene classification
Zhu Liu;Jincheng Huang;Yao Wang;Tsuhan Chen.
multimedia signal processing (1997)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: