Title :
A Novel approach for automatic ship type classification
Author :
Kacar, Umit ; Kumlu, Deniz ; Kirci, Murvet
Author_Institution :
Elektron. ve Haberlesme Muhendisligi Bolum, Istanbul Tek. Univ., İstanbul, Turkey
Abstract :
This work classifies the ship types from color images by using cameras mounted on ships. Our data set contains 10 different ship types. The synthetic images used for training imported from Google 3D Warehouse. Test data set imported from Google Images and contains real ship images. This work aims to classify real ship images by using synthetic images. We present a novel approach for combining four features extracted from synthetic images and we have achieved % 90 accuracy.
Keywords :
cameras; feature extraction; image classification; image colour analysis; marine engineering; object recognition; ships; Google 3D Warehouse; Google Images; automatic ship type classification; cameras; color images; feature extraction; real ship images; synthetic images; Color; Feature extraction; Google; Image recognition; Marine vehicles; Solid modeling; Ship type classification; feature combining; feature extraction; object recognition;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7130299