Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1668-1675.doi: 10.13229/j.cnki.jdxbgxb20180778

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Scene classification based on degree of naturalness and visual feature channels

Hong-wei ZHAO1,2(),Ming-zhao LI1,Jing LIU1,Huang-shui HU3,Dan WANG1,4,Xue-bai ZANG1()   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
    2. State Key Laboratory of Applied Optics, Chinese Academy of Sciences, Changchun 130033, China
    3. College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
    4. College of Information Technology and Media, Beihua University, Jilin 132013, China
  • Received:2018-09-23 Online:2019-09-01 Published:2019-09-11
  • Contact: Xue-bai ZANG E-mail:zhaohw@jlu.edu.cn;1047853240@qq.com

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Abstract:

In order to improve the recognition rate of scene classification and make it better used in target detection and behavior detection, an improved scene classification method is proposed. Firstly, the method uses naturalness in the Spatial Envelope to make the basic division of the scenes. Then, the visual feature channel model is applied to further classify the scenes in more details, resulting in the final semantic categories. Simulation results show that the improved scene classification method for semantic classification of scenes is superior to traditional scene classification method and has a higher recognition rate compared with other scene classification method.

Key words: computer vision, scene classification, visual feature channels, degree of naturalness, support vector machines

CLC Number: 

  • TP391.4

Fig.1

DST of natural degree"

Fig.2

Gist model based on visual feature channel"

Fig.3

Extraction process of Gist vector"

Fig.4

Improved classification process of Gist model"

Fig.5

Image library of natural scen"

Fig.6

Image library of man-made scen"

Fig.7

Recognition rate comparison between natural and man-made scene classification"

Fig.8

Semantic classification comparison of scenes"

Table 1

Confusion matrix based on global natural"

类别海滩森林山脉郊外公路中心街道大楼
海滩257.30002.7000
森林0227.5000.5000
山脉00296.404.500.10
郊外000309.10.9000
公路131.600.110.517.8000
中心380.60.600168.800
街道15.39.914.214.700137.90
大楼30.2030000222.8

Table 2

Confusion matrix based on localized natural degree and virtual feature channel model"

类别海滩森林山脉郊外公路中心街道大楼
海滩2600000000
森林0228000000
山脉0027400000
郊外0003100000
公路0000160000
中心0000020800
街道0000001920
大楼0000000224

Table 3

Comparison of different classification methods"

分类方法平均识别率/%
本文方法92.65
空域包络77.80
pLSA82.50
视觉词包84.50
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