D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 22,320 82 World Ranking 8266 National Ranking 14

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Ondrej Chum spends much of his time researching Artificial intelligence, Image retrieval, RANSAC, Information retrieval and Outlier. His Artificial intelligence research includes themes of Data mining and Pattern recognition. His Image retrieval study focuses on Visual Word in particular.

His work in RANSAC covers topics such as Sample which are related to areas like Image processing, Computer vision, Correctness and Function. In the field of Information retrieval, his study on Query expansion overlaps with subjects such as Vocabulary. His Outlier study deals with Robustness intersecting with Algorithm design, Machine learning, Harris affine region detector, Topology and Principal curvature-based region detector.

His most cited work include:

  • Robust wide-baseline stereo from maximally stable extremal regions (3019 citations)
  • Object retrieval with large vocabularies and fast spatial matching (2554 citations)
  • Lost in quantization: Improving particular object retrieval in large scale image databases (1270 citations)

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

Ondrej Chum mostly deals with Artificial intelligence, Pattern recognition, Image retrieval, Computer vision and Algorithm. His study looks at the relationship between Artificial intelligence and topics such as Affine transformation, which overlap with Focal length and Topology. Ondrej Chum has included themes like Object, Cartesian coordinate system and Scale in his Pattern recognition study.

His Image retrieval research incorporates elements of Matching, Query expansion, Information retrieval and Annotation. His Algorithm research includes elements of Image processing, RANSAC, Invariant and Group. Ondrej Chum has researched Epipolar geometry in several fields, including Similarity measure, Maximally stable extremal regions and Geometry.

He most often published in these fields:

  • Artificial intelligence (73.47%)
  • Pattern recognition (36.73%)
  • Image retrieval (35.71%)

What were the highlights of his more recent work (between 2017-2020)?

  • Artificial intelligence (73.47%)
  • Image retrieval (35.71%)
  • Pattern recognition (36.73%)

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

Ondrej Chum focuses on Artificial intelligence, Image retrieval, Pattern recognition, Algorithm and Graph. In his research on the topic of Artificial intelligence, Nearest neighbor graph is strongly related with Manifold. His Image retrieval study combines topics from a wide range of disciplines, such as Annotation, Matching, Information retrieval, Visualization and Convolutional neural network.

His work carried out in the field of Information retrieval brings together such families of science as Matching, Similarity and Digitization. His Pattern recognition study frequently links to adjacent areas such as Ranking. Algorithm is closely attributed to RANSAC in his study.

Between 2017 and 2020, his most popular works were:

  • Fine-Tuning CNN Image Retrieval with No Human Annotation (268 citations)
  • Label Propagation for Deep Semi-Supervised Learning (146 citations)
  • Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking (106 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

Ondrej Chum mainly focuses on Artificial intelligence, Image retrieval, Annotation, Information retrieval and Artificial neural network. The study incorporates disciplines such as Manifold, Algorithm and Pattern recognition in addition to Artificial intelligence. His work in Manifold addresses issues such as Nearest neighbor graph, which are connected to fields such as Online search, Ranking and Ranking SVM.

While the research belongs to areas of Pattern recognition, Ondrej Chum spends his time largely on the problem of Representation, intersecting his research to questions surrounding Object and Metric. His work focuses on many connections between Information retrieval and other disciplines, such as Visualization, that overlap with his field of interest in Search engine, Adversarial system and Image. His research investigates the connection between Artificial neural network and topics such as Deep learning that intersect with problems in Categorization, Transduction and Semi-supervised learning.

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.

Best Publications

Robust wide-baseline stereo from maximally stable extremal regions

Jiri Matas;Ondrej Chum;Martin Urban;Tomás Pajdla.
Image and Vision Computing (2004)

6260 Citations

Robust wide-baseline stereo from maximally stable extremal regions

Jiri Matas;Ondrej Chum;Martin Urban;Tomás Pajdla.
Image and Vision Computing (2004)

6260 Citations

Object retrieval with large vocabularies and fast spatial matching

J. Philbin;O. Chum;M. Isard;J. Sivic.
computer vision and pattern recognition (2007)

3470 Citations

Object retrieval with large vocabularies and fast spatial matching

J. Philbin;O. Chum;M. Isard;J. Sivic.
computer vision and pattern recognition (2007)

3470 Citations

Lost in quantization: Improving particular object retrieval in large scale image databases

J. Philbin;O. Chum;M. Isard;J. Sivic.
computer vision and pattern recognition (2008)

1761 Citations

Lost in quantization: Improving particular object retrieval in large scale image databases

J. Philbin;O. Chum;M. Isard;J. Sivic.
computer vision and pattern recognition (2008)

1761 Citations

Matching with PROSAC - progressive sample consensus

O. Chum;J. Matas.
computer vision and pattern recognition (2005)

1300 Citations

Matching with PROSAC - progressive sample consensus

O. Chum;J. Matas.
computer vision and pattern recognition (2005)

1300 Citations

Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval

O. Chum;J. Philbin;J. Sivic;M. Isard.
international conference on computer vision (2007)

1020 Citations

Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval

O. Chum;J. Philbin;J. Sivic;M. Isard.
international conference on computer vision (2007)

1020 Citations

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