Machine Learning for Rapid Image Classification
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
In this thesis project techniques for training a rapid image classifier that can recognize an object of a predefined type has been studied. Classifiers have been trained with the AdaBoost algorithm, with and without the use of Viola-Jones cascades. The use of Weight trimming in the classifier training has been evaluated and resulted in a significant speed up of the training, as well as improving the performance of the trained classifier. Different preprocessings of the images have also been tested, but resulted for the most part in worse performance for the classifiers when used individually. Several rectangle shaped Haar-like features including novel versions have been evaluated and the magnitude versions proved to be best at separating the image classes.
Place, publisher, year, edition, pages
2013. , 66 p.
Image Classification, Machine Learning, Computer Vision, AdaBoost, Viola-Jones, Weight Trimming, Haar-like features
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-97375ISRN: LiTH-IMT/MI30-A-EX--13/512--SEOAI: oai:DiVA.org:liu-97375DiVA: diva2:647269
Subject / course
2013-08-29, IMT1, Ingång 65, Campus US, Linköping, 10:15 (Swedish)
Andersson, Mats, 1:e Forskningsingenjör och Teknologie doktorBrandtberg, Tomas
Knutsson, Hans, Professor och Teknologie doktor