Fish Classification: Fish Classification Using Memetic Algorithms with Back Propagation Classifier - Tapa blanda

Alsmadi, Mutasem; Omar, Khairuddin; Almarashdeh, Ibrahim

 
9783848421671: Fish Classification: Fish Classification Using Memetic Algorithms with Back Propagation Classifier
Tapa blanda
ISBN 10: 3848421674 ISBN 13: 9783848421671
Editorial: LAP LAMBERT Academic Publishing, 2012

Sinopsis

This work presents a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose feature descriptors and do not analyze their individual impacts in the whole classification task. A problem with classification of fish species is still vital facets due to: arbitrary fish size and orientation; feature variability; environmental changes; poor image quality; segmentation failures; imaging conditions; physical shaping; distortion; noise; overlap, and occlusion of objects in digital images. In addition, the problem in fish classification is to find meaningful features based on the image segmentation and features extraction, and an efficient classifier that produces a better fish images classification accuracy rate. Thus, this research aims to design and develop a novel fish classifier based on an appropriate feature set obtained from image segmentation and features extraction methods, to classify the given fish output into its cluster (poison and non-poison fish), therefore; classifying the clustered poison and non-poison fish into its family.

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Reseña del editor

This work presents a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose feature descriptors and do not analyze their individual impacts in the whole classification task. A problem with classification of fish species is still vital facets due to: arbitrary fish size and orientation; feature variability; environmental changes; poor image quality; segmentation failures; imaging conditions; physical shaping; distortion; noise; overlap, and occlusion of objects in digital images. In addition, the problem in fish classification is to find meaningful features based on the image segmentation and features extraction, and an efficient classifier that produces a better fish images classification accuracy rate. Thus, this research aims to design and develop a novel fish classifier based on an appropriate feature set obtained from image segmentation and features extraction methods, to classify the given fish output into its cluster (poison and non-poison fish), therefore; classifying the clustered poison and non-poison fish into its family.

Biografía del autor

Mutasem Khalil Sari Al Smadi. He received his BS degree in Software engineering in 2006 from Philadelphia University, Jordan. His MSc degree in intelligent system in 2007 from University Utara Malaysia. His PhD degree in intelligent system in 2011 from University National Malaysia, Malaysia. He has published number of papers related to these areas.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2012
Idioma
Inglés
ISBN 10 
3848421674
ISBN 13 
9783848421671
Encuadernación
Tapa blanda
Número de páginas
180
Contacto del fabricante
no disponible
Persona responsable
no disponible