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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
Author
김기범
Issue Date
2020-07
Publisher
MDPI
Citation
SENSORS, v. 20, Issue. 14, Article no. 3871, 20pp
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.
URI
https://www.mdpi.com/1424-8220/20/14/3871https://repository.hanyang.ac.kr/handle/20.500.11754/164853
ISSN
1424-8220
DOI
10.3390/s20143871
Appears in Collections:
ETC[S] > 연구정보
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