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This thesis proposes a Visual Scene Change Detector(VSCD) system which is a system which involves four parts, image retrieval, image registration, image change detection and panorama creation. Two prestudies are conducted in order to find a proposed image registration method and a image retrieval method. The two found methods are then combined with a proposed image registration method and a proposed panorama creation method to form the proposed VSCD. The image retrieval prestudy evaluates a SIFT related method with a bag of words related method and finds the SIFT related method to be the superior method. The image change detection prestudy evaluates 8 different image change detection methods. Result from the image change detection prestudy shows that the methods performance is dependent on the image category and an ensemble method is the least dependent on the category of images. An ensemble method is found to be the best performing method followed by a range filter method and then a Convolutional Neural Network (CNN) method. Using a combination of the 2 image retrieval methods and the 8 image change detection method 16 different VSCD are formed and tested. The final result show that the VSCD comprised of the best methods from the prestudies is the best performing method., Detta exjobb föreslår ett Visual Scene Change Detector(VSCD) system vilket är ett system som har 4 delar, image retrieval, image registration, image change detection och panorama creation. Två förstudier görs för att hitta en föreslagen image registration metod och en föreslagen panorama creation metod. De två föreslagna delarna kombineras med en föreslagen image registration och en föreslagen panorama creation metod för att utgöra det föreslagna VSCD systemet. Image retrieval förstudien evaluerar en ScaleInvariant Feature Transform (SIFT) relaterad method med en Bag of Words (BoW) relaterad metod och hittar att den SIFT relaterade methoden är bäst. Image change detection förstudie visar att metodernas prestanda är beroende av catagorin av bilder och att en enemble metod är minst beroende av categorin av bilder. Enemble metoden är hittad att vara den bästa presterande metoden följt av en range filter metod och sedan av en CNN metod. Genom att använda de 2 image retrieval metoder kombinerat med de 8 image change detection metoder är 16 st VSCD system skapade och testade. Sista resultatet visar att den VSCD som använder de bästa metoderna från förstudien är den bäst presterande VSCD.

This thesis proposes a Visual Scene Change Detector(VSCD) system which is a system which involves four parts, image retrieval, image registration, image change detection and panorama creation. Two prestudies are conducted in order to find a proposed image registration method and a image retrieval method. The two found methods are then combined with a proposed image registration method and a proposed panorama creation method to form the proposed VSCD. The image retrieval prestudy evaluates a SIFT related method with a bag of words related method and finds the SIFT related method to be the superior method. The image change detection prestudy evaluates 8 different image change detection methods. Result from the image change detection prestudy shows that the methods performance is dependent on the image category and an ensemble method is the least dependent on the category of images. An ensemble method is found to be the best performing method followed by a range filter method and then a Convolutional Neural Network (CNN) method. Using a combination of the 2 image retrieval methods and the 8 image change detection method 16 different VSCD are formed and tested. The final result show that the VSCD comprised of the best methods from the prestudies is the best performing method., Detta exjobb föreslår ett Visual Scene Change Detector(VSCD) system vilket är ett system som har 4 delar, image retrieval, image registration, image change detection och panorama creation. Två förstudier görs för att hitta en föreslagen image registration metod och en föreslagen panorama creation metod. De två föreslagna delarna kombineras med en föreslagen image registration och en föreslagen panorama creation metod för att utgöra det föreslagna VSCD systemet. Image retrieval förstudien evaluerar en ScaleInvariant Feature Transform (SIFT) relaterad method med en Bag of Words (BoW) relaterad metod och hittar att den SIFT relaterade methoden är bäst. Image change detection förstudie visar att metodernas prestanda är beroende av catagorin av bilder och att en enemble metod är minst beroende av categorin av bilder. Enemble metoden är hittad att vara den bästa presterande metoden följt av en range filter metod och sedan av en CNN metod. Genom att använda de 2 image retrieval metoder kombinerat med de 8 image change detection metoder är 16 st VSCD system skapade och testade. Sista resultatet visar att den VSCD som använder de bästa metoderna från förstudien är den bäst presterande VSCD.

Imbalanced data problems are among the most challenging in Data Mining and Machine Learning research. This dissertation investigates the performance of ensemble learning systems on different types of data environments, and proposes novel ensemble learning approaches for solving imbalanced data problems. Bagging is one of the most effective ensemble methods for classification tasks. Despite the popularity of bagging in many real-world applications, there is a major drawback on extremely imbalanced data. Much research has addressed the problems of imbalanced data by using over-sampling and/or under-sampling methods to generate an equally balanced training set to improve the performance of the prediction models. However, it is unclear which is the best ratio for training, and under which conditions bagging is outperformed by other sampling schemes on extremely imbalanced data. Previous research has mainly been concerned with studying unstable learners as the key to ensuring the performance gain of a bagging predictor, with many key factors remaining unclear. Some questions have not been well answered: (1) What are the key factors for bagging predictors to achieve the best predictive performance for applications? and (2) What is the impact of varying the levels of class distribution on bagging predictors on different data environments. There is a lack of empirical investigation of these issues in the literature. The main contributions of this dissertation are as follows: 1. This dissertation proposes novel approaches, uneven balanced bagging to boost the performance of the prediction model for solving imbalanced problems, and hybrid-sampling to enhance bagging for solving highly imbalanced time series classification problems. 2. This dissertation asserts that robustness and stability are two key factors for building a high performance bagging predictor. This dissertation also derives a new method, utilizing two-dimensional robustness and stability decomposition to rank

Imbalanced data problems are among the most challenging in Data Mining and Machine Learning research. This dissertation investigates the performance of ensemble learning systems on different types of data environments, and proposes novel ensemble learning approaches for solving imbalanced data problems. Bagging is one of the most effective ensemble methods for classification tasks. Despite the popularity of bagging in many real-world applications, there is a major drawback on extremely imbalanced data. Much research has addressed the problems of imbalanced data by using over-sampling and/or under-sampling methods to generate an equally balanced training set to improve the performance of the prediction models. However, it is unclear which is the best ratio for training, and under which conditions bagging is outperformed by other sampling schemes on extremely imbalanced data. Previous research has mainly been concerned with studying unstable learners as the key to ensuring the performance gain of a bagging predictor, with many key factors remaining unclear. Some questions have not been well answered: (1) What are the key factors for bagging predictors to achieve the best predictive performance for applications? and (2) What is the impact of varying the levels of class distribution on bagging predictors on different data environments. There is a lack of empirical investigation of these issues in the literature. The main contributions of this dissertation are as follows: 1. This dissertation proposes novel approaches, uneven balanced bagging to boost the performance of the prediction model for solving imbalanced problems, and hybrid-sampling to enhance bagging for solving highly imbalanced time series classification problems. 2. This dissertation asserts that robustness and stability are two key factors for building a high performance bagging predictor. This dissertation also derives a new method, utilizing two-dimensional robustness and stability decomposition to rank