What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Qifa Ke focuses on Artificial intelligence, Mathematical optimization, Computer vision, Visual Word and Pattern recognition.
His studies in Artificial intelligence integrate themes in fields like Paragraph and Linear subspace.
His study in the fields of Minification under the domain of Mathematical optimization overlaps with other disciplines such as Convex optimization, Quasiconvex function and Factorization.
His work on Sensor fusion as part of general Computer vision research is frequently linked to Laser scanning, Global Positioning System, Obstacle avoidance and Collision avoidance, thereby connecting diverse disciplines of science.
His Visual Word research incorporates elements of Search engine indexing, Automatic image annotation and Euclidean distance.
Qifa Ke specializes in Pattern recognition, namely Feature extraction.
His most cited work include:
- Robust L/sub 1/ norm factorization in the presence of outliers and missing data by alternative convex programming (515 citations)
- A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (423 citations)
- Bundling features for large scale partial-duplicate web image search (409 citations)
What are the main themes of his work throughout his whole career to date?
Qifa Ke spends much of his time researching Artificial intelligence, Computer vision, Information retrieval, Image and Feature extraction.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Linear subspace and Pattern recognition.
Qifa Ke combines topics linked to Search engine indexing with his work on Computer vision.
In his research on the topic of Information retrieval, Matching is strongly related with Set.
As a part of the same scientific family, he mostly works in the field of Feature extraction, focusing on Image capture and, on occasion, Recognition system.
His Visual Word research integrates issues from Inverted index, Bag-of-words model and Automatic image annotation.
He most often published in these fields:
- Artificial intelligence (47.78%)
- Computer vision (31.11%)
- Information retrieval (22.22%)
What were the highlights of his more recent work (between 2012-2019)?
- Artificial intelligence (47.78%)
- Information retrieval (22.22%)
- Natural language processing (5.56%)
In recent papers he was focusing on the following fields of study:
Qifa Ke mainly investigates Artificial intelligence, Information retrieval, Natural language processing, Search engine and Cluster analysis.
His Artificial intelligence research incorporates themes from Paragraph and Pointer.
His work on Multi-document summarization is typically connected to Reading as part of general Information retrieval study, connecting several disciplines of science.
His Cluster analysis research is multidisciplinary, incorporating perspectives in Point, Social search and Image retrieval.
His studies deal with areas such as Feature, Encoding, Contextual image classification, Image texture and Inverted index as well as Image retrieval.
His work carried out in the field of Algorithm brings together such families of science as Nearest neighbor search, Mathematical optimization, Linear subspace and Cartesian product.
Between 2012 and 2019, his most popular works were:
- A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (423 citations)
- Optimized Product Quantization for Approximate Nearest Neighbor Search (268 citations)
- Optimized Product Quantization (180 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His main research concerns Artificial intelligence, Information retrieval, Automatic image annotation, Visual Word and Mathematical optimization.
His Artificial intelligence research incorporates elements of Paragraph, Pointer and Natural language processing.
His Information retrieval research is multidisciplinary, relying on both Information needs, Sequence, Deep learning and Task.
His Automatic image annotation research integrates issues from Embedding, The Internet, Kernel and Cluster analysis.
His biological study spans a wide range of topics, including Inverted index, Euclidean distance, Pattern recognition and Image texture.
The study incorporates disciplines such as Vector quantization, Algorithm, Linear subspace, Cartesian product and Nearest neighbor search in addition to Mathematical optimization.
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.
