D-Index & Metrics Best Publications

D-Index & Metrics

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 32 Citations 6,781 200 World Ranking 7388 National Ranking 62

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Image retrieval and Self-organizing map. His Artificial intelligence research incorporates elements of Machine learning and Speech recognition. His biological study spans a wide range of topics, including Artificial neural network and Feature.

Jorma Laaksonen specializes in Image retrieval, namely Content-based image retrieval. The concepts of his Content-based image retrieval study are interwoven with issues in Feature vector, Visual Word and Relevance feedback. His research investigates the connection between Self-organizing map and topics such as Vector quantization that intersect with issues in Tree and Euclidean distance.

His most cited work include:

  • SOM_PAK: The Self-Organizing Map Program Package (340 citations)
  • Variants of self-organizing maps (302 citations)
  • The 2005 PASCAL visual object classes challenge (280 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Information retrieval, Computer vision and Image retrieval. His research in Artificial intelligence focuses on subjects like Machine learning, which are connected to Classifier. The Pattern recognition study combines topics in areas such as Contextual image classification, Histogram, Speech recognition and Feature.

His Information retrieval research incorporates elements of Multimedia and TRECVID. The study incorporates disciplines such as Self-organizing map and Image texture in addition to Image retrieval. His Content-based image retrieval research is multidisciplinary, incorporating perspectives in Search engine indexing and Relevance feedback.

He most often published in these fields:

  • Artificial intelligence (60.64%)
  • Pattern recognition (33.69%)
  • Information retrieval (22.70%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (60.64%)
  • Pattern recognition (33.69%)
  • Computer vision (21.63%)

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

Jorma Laaksonen mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Image. His research in Artificial intelligence intersects with topics in Machine learning and Natural language processing. His Pattern recognition research integrates issues from Representation and Fixation.

His biological study deals with issues like Salience, which deal with fields such as Scheme, Mouse tracking and Field. His Convolutional neural network study incorporates themes from RGB color model, Pascal, Boltzmann machine and Feature. His study in Image is interdisciplinary in nature, drawing from both Base, Recall and Relation.

Between 2015 and 2021, his most popular works were:

  • Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification (98 citations)
  • Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation (72 citations)
  • Paying Attention to Descriptions Generated by Image Captioning Models (42 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Visualization. In his study, which falls under the umbrella issue of Artificial intelligence, Image is strongly linked to Context. His Pattern recognition study integrates concerns from other disciplines, such as Software and Fixation.

Jorma Laaksonen combines subjects such as Information retrieval and Salience with his study of Computer vision. His work in Visualization covers topics such as Feature extraction which are related to areas like Cognitive neuroscience of visual object recognition, Artificial neural network and Generator. His Convolutional neural network study combines topics from a wide range of disciplines, such as Texture, Local binary patterns, Pascal, Benchmark and RGB color model.

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

SOM_PAK: The Self-Organizing Map Program Package

T. Kohonen;J. Hynninen;J. Kangas;J. Laaksonen.
(1996)

523 Citations

Variants of self-organizing maps

J.A. Kangas;T.K. Kohonen;J.T. Laaksonen.
IEEE Transactions on Neural Networks (1990)

510 Citations

The 2005 PASCAL visual object classes challenge

Mark Everingham;Andrew Zisserman;Christopher K. I. Williams;Luc Van Gool.
international conference on machine learning (2005)

389 Citations

LVQ_PAK: The Learning Vector Quantization Program Package

T. Kohonen;J. Hynninen;J. Kangas;J. Laaksonen.
(1996)

356 Citations

PicSOM—content-based image retrieval with self-organizing maps

Jorma Laaksonen;Markus Koskela;Sami Laakso;Erkki Oja.
scandinavian conference on image analysis (2000)

318 Citations

PicSOM-self-organizing image retrieval with MPEG-7 content descriptors

J. Laaksonen;M. Koskela;E. Oja.
IEEE Transactions on Neural Networks (2002)

276 Citations

LVQPAK: A software package for the correct application of Learning Vector Quantization algorithms

T. Kohonen;J. Kangas;J. Laaksonen;K. Torkkola.
international joint conference on neural network (1992)

218 Citations

Neural and statistical classifiers-taxonomy and two case studies

L. Holmstrom;P. Koistinen;J. Laaksonen;E. Oja.
IEEE Transactions on Neural Networks (1997)

188 Citations

Statistical shape features in content-based image retrieval

S. Brandt;J. Laaksonen;E. Oja.
international conference on pattern recognition (2000)

186 Citations

Statistical Shape Features for Content-Based Image Retrieval

Sami Brandt;Jorma Laaksonen;Erkki Oja.
Journal of Mathematical Imaging and Vision (2002)

184 Citations

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