Multi-Modal Curriculum Learning for Semi-Supervised Image Classification

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because t...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Gong, Chen [verfasserIn]

Tao, Dacheng

Maybank, Stephen J

Liu, Wei

Kang, Guoliang

Yang, Jie

Format:

Artikel

Sprache:

Englisch

Erschienen:

2016

Schlagwörter:

Semi-supervised learning

Pattern recognition

Multi-modal

Curriculum learning

Reliability

Image classification

Electronic mail

Image processing

Visualization

Semisupervised learning

Kernel

Management

Electronic mail systems

Object recognition (Computers)

Training

Iterative methods (Mathematics)

Teacher centers

Teachers

Usage

Übergeordnetes Werk:

Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, 25(2016), 7, Seite 3249-3260

Übergeordnetes Werk:

volume:25 ; year:2016 ; number:7 ; pages:3249-3260

Links:

Volltext
Link aufrufen

DOI / URN:

10.1109/TIP.2016.2563981

Katalog-ID:

OLC1979422788

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