Sparse coding with unity range codes and label consistent discriminative dictionary learning

2016 ◽  
Author(s):  
Mikael Nilsson
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Discriminative Dictionary Learning

scholarly journals Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

NeuroImage ◽  
2013 ◽  
Vol 76 ◽  
pp. 11-23 ◽  
Author(s):  
Tong Tong ◽  
Robin Wolz ◽  
Pierrick Coupé ◽  
Joseph V. Hajnal ◽  
Daniel Rueckert
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Mr Images ◽  
Discriminative Dictionary Learning

Layered convolutional dictionary learning for sparse coding itemsets

2018 ◽  
Vol 22 (5) ◽  
pp. 2225-2239
Author(s):  
Sameen Mansha ◽  
Hoang Thanh Lam ◽  
Hongzhi Yin ◽  
Faisal Kamiran ◽  
Mohsen Ali
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding

scholarly journals Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning

2019 ◽  
Vol 11 (7) ◽  
pp. 769 ◽  
Author(s):  
Huiping Lin ◽  
Hang Chen ◽  
Hongmiao Wang ◽  
Junjun Yin ◽  
Jian Yang
Keyword(s):  
Learning Strategies ◽  
Dictionary Learning ◽  
Binary Classification ◽  
Contextual Information ◽  
Polarimetric Synthetic Aperture Radar ◽  
Robust Detection ◽  
Ship Detection ◽  
Sparsity Constraints ◽  
Active Learning Strategies ◽  
Discriminative Dictionary Learning

Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.

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