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

1
Department of Computer Science and Engineering, Air University, E-9, Islamabad 44000, Pakistan
2
Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(14), 3871; https://doi.org/10.3390/s20143871
Received: 27 May 2020 / Revised: 24 June 2020 / Accepted: 8 July 2020 / Published: 10 July 2020
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: adaptive weighted median filter; fuzzy c-mean segmentation; logistic regression; multiple objects categorization; multiple kernel learning; scene classification; visual sensors adaptive weighted median filter; fuzzy c-mean segmentation; logistic regression; multiple objects categorization; multiple kernel learning; scene classification; visual sensors
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Ahmed, A.; Jalal, A.; Kim, K. A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression. Sensors 2020, 20, 3871.

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