Jorma Laaksonen
Jorma Laaksonen
Senior University Lecturer in Computer Science, Aalto University
Verified email at - Homepage
Cited by
Cited by
SOM_PAK: The self-organizing map program package
T Kohonen, J Hynninen, J Kangas, J Laaksonen
Technical report 31, 1996, 1996
Variants of self-organizing maps
Kangas, Kohonen, Laaksonen, Simula, Venta
International 1989 Joint Conference on Neural Networks, 517-522 vol. 2, 1989
LVQ PAK: The learning vector quantization program package
T Kohonen, J Hynninen, J Kangas, J Laaksonen, K Torkkola
Technical report, Laboratory of computer and Information Science …, 1996
The 2005 pascal visual object classes challenge
M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ...
Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006
PicSOM–content-based image retrieval with self-organizing maps
J Laaksonen, M Koskela, S Laakso, E Oja
Pattern recognition letters 21 (13-14), 1199-1207, 2000
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
J Laaksonen, M Koskela, E Oja
IEEE Transactions on Neural Networks 13 (4), 841-853, 2002
Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification
RM Anwer, FS Khan, J Van De Weijer, M Molinier, J Laaksonen
ISPRS journal of photogrammetry and remote sensing 138, 74-85, 2018
Classification with learning k-nearest neighbors
J Laaksonen, E Oja
Proceedings of international conference on neural networks (ICNN'96) 3, 1480 …, 1996
LVQ PAK: A program package for the correct application of Learning Vector Quantization algorithms
T Kohonen, J Kangas, J Laaksonen, K Torkkola
Proc. IJCNN 92, 725-730, 1992
Neural and statistical classifiers-taxonomy and two case studies
L Holmstrom, P Koistinen, J Laaksonen, E Oja
IEEE Transactions on Neural Networks 8 (1), 5-17, 1997
Statistical shape features for content-based image retrieval
S Brandt, J Laaksonen, E Oja
Journal of Mathematical Imaging and Vision 17, 187-198, 2002
Self-organising maps as a relevance feedback technique in content-based image retrieval
J Laaksonen, M Koskela, S Laakso, E Oja
Pattern Analysis & Applications 4, 140-152, 2001
Deep contextual attention for human-object interaction detection
T Wang, RM Anwer, MH Khan, FS Khan, Y Pang, L Shao, J Laaksonen
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Using diversity of errors for selecting members of a committee classifier
M Aksela, J Laaksonen
Pattern Recognition 39 (4), 608-623, 2006
An augmented reality interface to contextual information
A Ajanki, M Billinghurst, H Gamper, T Järvenpää, M Kandemir, S Kaski, ...
Virtual reality 15, 161-173, 2011
Picsom: Self-organizing maps for content-based image retrieval
J Laaksonen, M Koskela, E Oja
IJCNN'99. International Joint Conference on Neural Networks. Proceedings …, 1999
Frame-and segment-level features and candidate pool evaluation for video caption generation
R Shetty, J Laaksonen
Proceedings of the 24th ACM international conference on Multimedia, 1073-1076, 2016
Paying attention to descriptions generated by image captioning models
HR Tavakoli, R Shetty, A Borji, J Laaksonen
Proceedings of the IEEE international conference on computer vision, 2487-2496, 2017
The MeMAD submission to the WMT18 multimodal translation task
SA Grönroos, B Huet, M Kurimo, J Laaksonen, B Merialdo, P Pham, ...
arXiv preprint arXiv:1808.10802, 2018
Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features
HR Tavakoli, A Borji, J Laaksonen, E Rahtu
Neurocomputing 244, 10-18, 2017
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