Farid Melgani

Computer Vision and Pattern Recognition · 2004–2018

A researcher in the Science corpus: 8 papers; trusts 154 researchers and is trusted by 159 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #2,875 of 21,575 by trust here, #3,692 by citations
• Artificial Intelligence — 41 citations into it
• Media Technology — 26 citations into it; #68 of 3,621 by trust here, #188 by citations
• Plant Science — 19 citations into it
• Ecology — 17 citations into it
• Cognitive Neuroscience — 15 citations into it; #4,232 of 10,852 by trust here, #5,043 by citations
• Cardiology and Cardiovascular Medicine — 14 citations into it; #168 of 906 by trust here, #358 by citations
• Atmospheric Science — 11 citations into it; #28 of 4,804 by trust here, #323 by citations

Whom Farid Melgani trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Vladimir Naumovich Vapnik
2. Yuhai Wu
3. M. Stone
4. Lorenzo Bruzzone
5. Philip de Chazal
6. Stanislaw Osowski
7. Sebastiano Bruno Serpico
8. Olivier Chapelle
9. Bernhard Schölkopf
10. Geoffrey E. Hinton

Who trusts Farid Melgani overall

1. Lorenzo Bruzzone
2. Jon Atli Benediktsson
3. Shutao Li
4. Swalpa Kumar Roy
5. Abdulhamit Subasi
6. Leyuan Fang
7. Yakoub Bazi
8. Pedram Ghamisi
9. Björn Waske
10. Gustau Camps-Valls

Papers from Farid Melgani's perspective

Ordered by how strongly the researchers Farid Melgani trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Land-Use Classification With Compressive Sensing Multifeature Fusion (2015) — 75 citations
• Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018) — 175 citations
• Deep learning approach for active classification of electrocardiogram signals (2016) — 601 citations
• Using convolutional features and a sparse autoencoder for land-use scene classification (2016) — 153 citations
• Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008) — 506 citations
• Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (2006) — 456 citations
• An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images (2005) — 751 citations
• Classification of hyperspectral remote sensing images with support vector machines (2004) — 4,077 citations