Statistical feature bag based background subtraction for local change detection

Subudhi, Bidyadhar; Ghosh, Susmita; Shiu, Simon; Ghosh, Ashish

doi:10.1016/j.ins.2016.04.049

Cited by 31 publications

(11 citation statements)

References 52 publications

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“…➢ First criterion : There are various data partition performance evaluation methods such as hold-out and K-fold cross-validation in the literature [ 13 , 19 ]. The following items should be taken into consideration while distinguishing the data set as training and testing:…”

Section: Methodsmentioning

confidence: 99%

Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer

Toprak

2018

Med Sci Monit

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BackgroundBreast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI).Material/MethodsDetection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database.ResultsThe results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%.ConclusionsIt was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features.

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Section: Methodsmentioning

confidence: 99%

Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer

Toprak

2018

Med Sci Monit

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“…The use of multiple features (also called Bag-of-Features (BoF) [583][307] [478]) for background modeling has become a promising solution to improving robustness in real applications. The fundamental idea is to add spatial and/or temporal dimensions to the already existing spectral information available from the visual scene.…”

Section: Multiple Featuresmentioning

confidence: 99%

“…More than three features can be found in ensemble of features based approaches [276][180] [327], feature selection schemes (See Section 17) or bag-of-features approaches [583][307] [478]. For example, Klare and Sarkar [276] proposed an algorithm that incorporates multiple instantiations of the MOG algorithm with 13 features including: 1) color features (RGB), and 2) edge features which are the gradient and magnitude obtained with a Canny edge detector, eight texture feature (Haar features).…”

Section: More Than Two Featuresmentioning

confidence: 99%

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

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Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in T. Bouwmans Lab. MIA,

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“…El método de sustracción de fondo no se suele utilizar debido a sus pobres resultados. Sin embargo, los autores en [118] usan con éxito este método de sustracción de fondo para detectar cambios locales de movimientos. Asimismo, los autores en [119] lo aplican a la detección de sombra.…”

Section: Estado Del Arteunclassified

“…La tabla 1.1 hace un resumen general de los métodos analizados en la categoría de Álgebra con sus características principales, ventajas, desventajas y puntos clave. [118,119] Se filtra paso bajo la imagen original y se le resta al original…”

Section: Estado Del Arteunclassified

Técnicas de detección de cambios mediante teledetección para el desarrollo sostenible y la desertificación

Azzouzi¹

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ligue islamique et au centre culturel islamique de Valencia (CCIV), à tous mes collègues de la société EDICOM à tous mes amis et à tous les personnes qui m'ont aidé de près ou de loin... "Ô mon Seigneur, accroît mes connaissances!" Coran (20/114) Agradecimientos Tras muchos años de trabajo, esto parece que por fin tiene un punto final, y no podría haberlo conseguido sin la ayuda, el apoyo, el consejo y el soporte de todos vosotros. Ante todo, quiero agradecer enormemente a mi directora Dr. Ana Vidal Pantaleoni por su apoyo, paciencia, atención y disponibilidad durante estos años, por darme la oportunidad de desarrollar mi carrera investigadora con ella en el Grupo de Aplicaciones de las Microondas (GAM). Muchísimas gracias. De igual manera quiero agradecer mi codirector Dr. Hadj Adda Bentounes por su labor en el desarrollo del presente trabajo. A la unión europea por su excelente programa de Erasmus Mundus (EMMAG),

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Statistical feature bag based background subtraction for local change detection

Cited by 31 publications

References 52 publications

Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer

Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Técnicas de detección de cambios mediante teledetección para el desarrollo sostenible y la desertificación

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