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A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering...

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Detalles Bibliográficos
Autores principales: Ahmed, Abrar, Jalal, Ahmad, Kim, Kibum
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411670/
https://www.ncbi.nlm.nih.gov/pubmed/32664434
http://dx.doi.org/10.3390/s20143871
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author Ahmed, Abrar
Jalal, Ahmad
Kim, Kibum
author_facet Ahmed, Abrar
Jalal, Ahmad
Kim, Kibum
author_sort Ahmed, Abrar
collection PubMed
description In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.
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spelling pubmed-74116702020-08-25 A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression Ahmed, Abrar Jalal, Ahmad Kim, Kibum Sensors (Basel) Article In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications. MDPI 2020-07-10 /pmc/articles/PMC7411670/ /pubmed/32664434 http://dx.doi.org/10.3390/s20143871 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ahmed, Abrar
Jalal, Ahmad
Kim, Kibum
A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression
title A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression
title_full A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression
title_fullStr A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression
title_full_unstemmed A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression
title_short A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression
title_sort novel statistical method for scene classification based on multi-object categorization and logistic regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411670/
https://www.ncbi.nlm.nih.gov/pubmed/32664434
http://dx.doi.org/10.3390/s20143871
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