What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Image retrieval, Pattern recognition, Computer vision and Image processing.
As part of his studies on Artificial intelligence, James Z. Wang frequently links adjacent subjects like Machine learning.
James Z. Wang combines subjects such as Information retrieval and Visual arts with his study of Image retrieval.
His Information retrieval study combines topics in areas such as Annotation, Cluster analysis and Identification.
His work deals with themes such as Contextual image classification and Fuzzy mathematics, which intersect with Pattern recognition.
His study in Computer vision is interdisciplinary in nature, drawing from both Word and CAPTCHA.
His most cited work include:
- Image retrieval: Ideas, influences, and trends of the new age (2794 citations)
- SIMPLIcity: semantics-sensitive integrated matching for picture libraries (1991 citations)
- Automatic Linguistic Indexing of Pictures by a statistical modeling approach (1003 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Image retrieval, Computer vision, Pattern recognition and Information retrieval.
His work in Artificial intelligence addresses issues such as Machine learning, which are connected to fields such as Categorization and Aesthetics.
His Image retrieval study combines topics in areas such as Annotation and Search engine indexing.
James Z. Wang has included themes like Image and Statistical model in his Pattern recognition study.
His Information retrieval research is multidisciplinary, relying on both World Wide Web and The Internet.
His research investigates the connection between Automatic image annotation and topics such as Image texture that intersect with issues in Feature detection.
He most often published in these fields:
- Artificial intelligence (48.44%)
- Image retrieval (27.73%)
- Computer vision (23.44%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (48.44%)
- Computer vision (23.44%)
- Pattern recognition (20.31%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Deep learning are his primary areas of study.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Categorization.
His Computer vision research includes elements of Robotics and Composition.
His Pixel research extends to the thematically linked field of Pattern recognition.
His Image retrieval study deals with the bigger picture of Image.
His Image research integrates issues from Aesthetics and Cognitive psychology.
Between 2014 and 2021, his most popular works were:
- Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation (176 citations)
- Rating Image Aesthetics Using Deep Learning (129 citations)
- Microexpression Identification and Categorization Using a Facial Dynamics Map (116 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
James Z. Wang spends much of his time researching Artificial intelligence, Pattern recognition, Convolutional neural network, Computer vision and Deep learning.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His research on Machine learning also deals with topics like
- Categorization which connect with Image,
- Aesthetics together with Boltzmann machine, Joint, Representation and Image retrieval.
His work deals with themes such as Edge detection and Spatial contextual awareness, which intersect with Pattern recognition.
His study in Computer vision is interdisciplinary in nature, drawing from both Portrait, Digital photography, Position and Lens.
As part of one scientific family, he deals mainly with the area of Deep learning, narrowing it down to issues related to the Artificial neural network, and often Algorithm.
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