Arnold W. M. Smeulders

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Computer vision
• Statistics

Arnold W. M. Smeulders mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and TRECVID. His study in Robustness, Codebook, Image processing, Object and Segmentation falls within the category of Artificial intelligence. While the research belongs to areas of Computer vision, Arnold W. M. Smeulders spends his time largely on the problem of Invariant, intersecting his research to questions surrounding Discriminative model.

His Pattern recognition research includes elements of Contextual image classification, Cognitive neuroscience of visual object recognition, Outcome and Contrast. His Image retrieval research incorporates elements of Feature extraction, Color model, Search engine indexing and Image texture. His Information retrieval study incorporates themes from Machine learning and Relevance feedback.

His most cited work include:

• Content-based image retrieval at the end of the early years (5505 citations)
• Selective Search for Object Recognition (3740 citations)
• Visual Tracking: An Experimental Survey (1202 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Information retrieval and Image processing. Many of his studies on Artificial intelligence apply to Machine learning as well. Computer vision is frequently linked to Invariant in his study.

His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification, Feature, Cognitive neuroscience of visual object recognition and Categorization. His Search engine, Search engine indexing, Ranking and Video retrieval study in the realm of Information retrieval connects with subjects such as TRECVID. His study in Visual Word, Automatic image annotation and Content-based image retrieval is carried out as part of his studies in Image retrieval.

He most often published in these fields:

• Artificial intelligence (56.56%)
• Computer vision (29.86%)
• Pattern recognition (21.04%)

What were the highlights of his more recent work (between 2017-2021)?

• Artificial intelligence (56.56%)
• Computer vision (29.86%)
• Machine learning (7.92%)

In recent papers he was focusing on the following fields of study:

His main research concerns Artificial intelligence, Computer vision, Machine learning, Pattern recognition and Algorithm. His Object, Video tracking, Robustness, Artificial neural network and Tracking study are his primary interests in Artificial intelligence. His work in the fields of Computer vision, such as Motion, overlaps with other areas such as Dynamics.

His studies in Pattern recognition integrate themes in fields like Focus, Convolution, Translation and Invariant. His research investigates the connection with Algorithm and areas like Function which intersect with concerns in Measure and Iterative refinement. His work in Discriminative model addresses subjects such as Filter, which are connected to disciplines such as Cognitive neuroscience of visual object recognition.

Between 2017 and 2021, his most popular works were:

• The sixth visual object tracking VOT2018 challenge results (299 citations)
• The Seventh Visual Object Tracking VOT2019 Challenge Results (122 citations)
• i-RevNet: Deep Invertible Networks (79 citations)

In his most recent research, the most cited papers focused on:

• Artificial intelligence
• Statistics
• Computer vision

Arnold W. M. Smeulders mostly deals with Artificial intelligence, Video tracking, Algorithm, Object and Theoretical computer science. His Artificial intelligence study combines topics in areas such as Contrast, Tuple and Pattern recognition. His Pattern recognition research is multidisciplinary, incorporating perspectives in Convolution and Outcome.

His Video tracking study is concerned with Computer vision in general. His work in Computer vision tackles topics such as Source code which are related to areas like RGB color model and Robustness. His research integrates issues of Machine learning, Field, Tracking and Benchmark in his study of Object.