Paper Info
Title
A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression.
Abstract
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.
Year
DOI
Venue
2020
10.3390/s20143871
SENSORS
Keywords
DocType
Volume
adaptive weighted median filter,fuzzy c-mean segmentation,logistic regression,multiple objects categorization,multiple kernel learning,scene classification,visual sensors
Journal
20
Issue
ISSN
Citations 
14
1424-8220
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Abrar Ahmed100.34
Ahmad Jalal215311.00
Kibum Kim313618.81