What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- The Internet
Shipeng Li mostly deals with Artificial intelligence, Computer vision, Motion compensation, Multimedia and Scalable Video Coding.
Artificial intelligence connects with themes related to Pattern recognition in his study.
Shipeng Li has included themes like Reference frame and Sub-band coding in his Computer vision study.
His work deals with themes such as Motion estimation, Frame rate, Computer graphics and Block-matching algorithm, which intersect with Motion compensation.
His Multimedia study combines topics from a wide range of disciplines, such as Service, The Internet, World Wide Web, Peer-to-peer and Smacker video.
His studies in Scalable Video Coding integrate themes in fields like Multiview Video Coding, Bitstream, Coding tree unit and Real-time computing.
His most cited work include:
- Salient Object Detection: A Discriminative Regional Feature Integration Approach (864 citations)
- Multimedia Cloud Computing (364 citations)
- A framework for efficient progressive fine granularity scalable video coding (314 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Motion compensation, Algorithm and Multimedia.
As a member of one scientific family, Shipeng Li mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Machine learning.
His study in Computer vision is interdisciplinary in nature, drawing from both Encoder and Coding tree unit.
The Motion compensation study combines topics in areas such as Macroblock, Rate–distortion optimization, Block-matching algorithm and Transform coding.
The various areas that Shipeng Li examines in his Multimedia study include The Internet, World Wide Web and Mobile device.
His Multiview Video Coding research includes themes of Scalable Video Coding and Video compression picture types.
He most often published in these fields:
- Artificial intelligence (42.27%)
- Computer vision (34.77%)
- Motion compensation (14.55%)
What were the highlights of his more recent work (between 2011-2020)?
- Artificial intelligence (42.27%)
- Computer vision (34.77%)
- Image retrieval (4.77%)
In recent papers he was focusing on the following fields of study:
Shipeng Li mainly investigates Artificial intelligence, Computer vision, Image retrieval, Information retrieval and Algorithm.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
His Computer vision research includes elements of Codec and Encoder.
The study incorporates disciplines such as Visualization, Feature extraction and Cluster analysis in addition to Image retrieval.
His Information retrieval course of study focuses on Visual search and Session, Mobile computing, User interface, Web page and User experience design.
His Algorithm research incorporates elements of Most significant bit, Nearest neighbor search, k-means clustering and Algorithmic efficiency.
Between 2011 and 2020, his most popular works were:
- Salient Object Detection: A Discriminative Regional Feature Integration Approach (864 citations)
- Multimedia search reranking: A literature survey (156 citations)
- Order preserving hashing for approximate nearest neighbor search (113 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- The Internet
Artificial intelligence, Computer vision, Multimedia, Machine learning and Theoretical computer science are his primary areas of study.
His biological study spans a wide range of topics, including Location-based service and Pattern recognition.
His work focuses on many connections between Pattern recognition and other disciplines, such as Object detection, that overlap with his field of interest in Feature.
His Process research extends to the thematically linked field of Computer vision.
His Multimedia research incorporates themes from Transfer of learning, Social media and Mobile computing.
His research in Theoretical computer science intersects with topics in k-means clustering, Cluster analysis, k-nearest neighbors algorithm, Graph and Nearest neighbor search.
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.
