What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Image retrieval, Pattern recognition, Content-based image retrieval and Computer vision.
His study in Machine learning extends to Artificial intelligence with its themes.
Ricardo da Silva Torres interconnects Information retrieval and Feature vector in the investigation of issues within Image retrieval.
His Search engine indexing study in the realm of Information retrieval interacts with subjects such as Quality.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Subspace topology and Test set.
His Content-based image retrieval research includes elements of Similarity, Data mining and Rank.
His most cited work include:
- Comparative study of global color and texture descriptors for web image retrieval (156 citations)
- A genetic programming framework for content-based image retrieval (151 citations)
- Content-Based Image Retrieval: Theory and Applications. (150 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Image retrieval, Information retrieval and Machine learning are his primary areas of study.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His work carried out in the field of Pattern recognition brings together such families of science as Data mining and Time series.
The concepts of his Image retrieval study are interwoven with issues in Rank and Image texture.
His research integrates issues of Annotation and Multimedia in his study of Information retrieval.
His studies deal with areas such as Phenology and Relevance feedback as well as Machine learning.
He most often published in these fields:
- Artificial intelligence (52.72%)
- Pattern recognition (29.93%)
- Image retrieval (25.85%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (52.72%)
- Pattern recognition (29.93%)
- Machine learning (17.69%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Theoretical computer science and Contextual image classification.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
His study in the fields of Feature vector under the domain of Pattern recognition overlaps with other disciplines such as Series.
His Feature vector study combines topics in areas such as Quantization, Genetic algorithm, Curse of dimensionality and Image retrieval.
His work investigates the relationship between Machine learning and topics such as Natural disaster that intersect with problems in Flood myth.
His Theoretical computer science research incorporates elements of Match analysis, Football and Graph based.
Between 2017 and 2021, his most popular works were:
- Graph-based bag-of-words for classification (35 citations)
- Semi-supervised transfer subspace for domain adaptation (30 citations)
- Exploiting ConvNet Diversity for Flooding Identification (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Remote sensing and Contextual image classification.
His research on Artificial intelligence often connects related topics like Graph.
In his study, Pairwise comparison, Ranking, Image retrieval, Manifold and Multimedia is inextricably linked to Cluster analysis, which falls within the broad field of Graph.
His work in the fields of Segmentation, Support vector machine and Multiclass classification overlaps with other areas such as Object based.
His work is dedicated to discovering how Machine learning, Robustness are connected with Majority rule, Outlier, Sensor fusion and Genetic programming and other disciplines.
His Contextual image classification research integrates issues from Bag-of-words model and Matching.
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