D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

2021 ◽  
Author(s):  
Majid Ghasemi ◽  
Manoochehr Kelarestaghi ◽  
Farshad Eshghi ◽  
Arash Sharifi
Keyword(s):  
Medical Imaging ◽  
Dictionary Learning ◽  
Fuzzy Classifier ◽  
Deep Feature ◽  
Feature Extractor ◽  
Discriminative Dictionary Learning

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.

Robust and discriminative dictionary learning for face recognition

2018 ◽  
Vol 16 (02) ◽  
pp. 1840004 ◽  
Author(s):  
Guojun Lin ◽  
Meng Yang ◽  
Linlin Shen ◽  
Mingzhong Yang ◽  
Mei Xie
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Dictionary Learning ◽  
State Of The Art ◽  
Face Image ◽  
Superior Performance ◽  
Face Images ◽  
Proposed Model ◽  
Classification Capability ◽  
Discriminative Dictionary Learning

For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don’t cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

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