Alexander G. Hauptmann mostly deals with Artificial intelligence, Machine learning, Information retrieval, Pattern recognition and TRECVID. His Artificial intelligence research is multidisciplinary, incorporating elements of Multimedia and Computer vision. His studies in Multimedia integrate themes in fields like World Wide Web and Selection.
His Machine learning research includes themes of Classifier and Data mining. His Information retrieval research incorporates elements of Context and Image retrieval, Relevance feedback. He has researched Pattern recognition in several fields, including Precision and recall, Object detection, Feature and Boosting.
Artificial intelligence, Information retrieval, Machine learning, Multimedia and TRECVID are his primary areas of study. His Artificial intelligence research incorporates themes from Natural language processing, Computer vision and Pattern recognition. His Pattern recognition research is multidisciplinary, relying on both Contextual image classification and Image.
His Information retrieval study combines topics in areas such as Metadata, Visual Word, Image retrieval and Video tracking. His work deals with themes such as Classifier, Representation and Training set, which intersect with Machine learning. Alexander G. Hauptmann integrates TRECVID and Data mining in his studies.
His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Information retrieval and Multimedia. His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Natural language processing. The Convolutional neural network and Feature research Alexander G. Hauptmann does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Trajectory and Modal, therefore creating a link between diverse domains of science.
In his research on the topic of Pattern recognition, Context is strongly related with Image. He combines Information retrieval and TRECVID in his research. He combines subjects such as Natural language and Feature with his study of Representation.
Alexander G. Hauptmann mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Feature extraction and Information retrieval. The Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing. His work on Feature as part of general Machine learning study is frequently linked to TRECVID and Path, therefore connecting diverse disciplines of science.
His Pattern recognition research integrates issues from Computational complexity theory, Outlier and Action. His work in Feature extraction covers topics such as Artificial neural network which are related to areas like Pattern recognition. The study incorporates disciplines such as Context, Multimedia and Metadata in addition to Information retrieval.
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.
Evaluating bag-of-visual-words representations in scene classification
Jun Yang;Yu-Gang Jiang;Alexander G. Hauptmann;Chong-Wah Ngo.
multimedia information retrieval (2007)
Large-scale concept ontology for multimedia
M. Naphade;J.R. Smith;J. Tesic;Shih-Fu Chang.
IEEE MultiMedia (2006)
Cross-domain video concept detection using adaptive svms
Jun Yang;Rong Yan;Alexander G. Hauptmann.
acm multimedia (2007)
Person Re-identification: Past, Present and Future
Liang Zheng;Yi Yang;Alexander G. Hauptmann.
arXiv: Computer Vision and Pattern Recognition (2016)
A discriminative CNN video representation for event detection
Zhongwen Xu;Yi Yang;Alexander G. Hauptmann.
computer vision and pattern recognition (2015)
Practical elimination of near-duplicates from web video search
Xiao Wu;Alexander G. Hauptmann;Chong-Wah Ngo.
acm multimedia (2007)
MoSIFT: Recognizing Human Actions in Surveillance Videos
Ming-Yu Chen;Alexander Hauptmann.
Lessons learned from building a terabyte digital video library
H.D. Wactlar;M.G. Christel;Yihong Gong;A.G. Hauptmann.
IEEE Computer (1999)
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
Yu-Gang Jiang;Jun Yang;Chong-Wah Ngo;A.G. Hauptmann.
IEEE Transactions on Multimedia (2010)
Which Side are You on? Identifying Perspectives at the Document and Sentence Levels
Wei-Hao Lin;Theresa Wilson;Janyce Wiebe;Alexander Hauptmann.
conference on computational natural language learning (2006)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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: