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.
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.
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.
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.
SOM_PAK: The Self-Organizing Map Program Package
T. Kohonen;J. Hynninen;J. Kangas;J. Laaksonen.
Variants of self-organizing maps
J.A. Kangas;T.K. Kohonen;J.T. Laaksonen.
IEEE Transactions on Neural Networks (1990)
The 2005 PASCAL visual object classes challenge
Mark Everingham;Andrew Zisserman;Christopher K. I. Williams;Luc Van Gool.
international conference on machine learning (2005)
LVQ_PAK: The Learning Vector Quantization Program Package
T. Kohonen;J. Hynninen;J. Kangas;J. Laaksonen.
PicSOM—content-based image retrieval with self-organizing maps
Jorma Laaksonen;Markus Koskela;Sami Laakso;Erkki Oja.
scandinavian conference on image analysis (2000)
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
J. Laaksonen;M. Koskela;E. Oja.
IEEE Transactions on Neural Networks (2002)
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)
Neural and statistical classifiers-taxonomy and two case studies
L. Holmstrom;P. Koistinen;J. Laaksonen;E. Oja.
IEEE Transactions on Neural Networks (1997)
Statistical shape features in content-based image retrieval
S. Brandt;J. Laaksonen;E. Oja.
international conference on pattern recognition (2000)
Statistical Shape Features for Content-Based Image Retrieval
Sami Brandt;Jorma Laaksonen;Erkki Oja.
Journal of Mathematical Imaging and Vision (2002)
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