An Image Saliency Detection Method Based on Combining Global and Local Information

被引:0
|
作者
Yang, Hangxu [1 ,2 ]
Gong, Yongjian [1 ]
Wang, Kai [3 ]
机构
[1] Jinhua Polytech, Coll Mech & Elect Engn, Jinhua 321017, Zhejiang, Peoples R China
[2] Key Lab Crop Harvesting Equipment Technol Zhejian, Jinhua 321017, Zhejiang, Peoples R China
[3] Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China
关键词
Compendex;
D O I
10.1155/2022/1849995
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the field of computer vision, image saliency target detection can not only improve the accuracy of image detection but also accelerate the speed of image detection. In order to solve the existing problems of the saliency target detection algorithms at present, such as inconspicuous texture details and incomplete edge contour display, this paper proposes a saliency target detection algorithm integrating multiple information. The algorithm consists of three processes: preprocessing process, multi-information extraction process, and fusion optimization process. The frequency domain features of the image are calculated, the algorithm calculates the frequency domain features of the image, introduces power law transform and feature normalization, improves the frequency domain features of the image, saves the information of the target region, and inhibits the information of the background region. On three public MSRA, SED2, and ECSSD image datasets, the proposed algorithm is compared with other classical algorithms in subjective and objective comparison experiments. Experimental results show that the proposed algorithm can not only accurately and comprehensively extract significant target regions but also retain more texture information and complete edge information while satisfying the human visual experience. All evaluation indexes are significantly better than the comparison algorithm, showing good reliability and adaptability.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Saliency Detection Using Global and Local Information Under Multilayer Cellular Automata
    Liu, Yihang
    Yuan, Peiyan
    IEEE ACCESS, 2019, 7 : 72736 - 72748
  • [22] On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
    Wang Jing
    Wang Leqi
    Han Yanling
    Zhang Yun
    Zhou Ruyan
    APPLIED SCIENCES-BASEL, 2021, 11 (14):
  • [23] Image fusion combining FABEMD with improved saliency detection
    An Y.
    Fan X.
    Chen L.
    Liu P.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2020, 42 (02): : 292 - 300
  • [24] A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency
    Nan, Bingfei
    Mu, Zhichun
    Chen, Long
    Cheng, Jian
    APPLIED SCIENCES-BASEL, 2015, 5 (04): : 1528 - 1546
  • [25] Exploiting local and global characteristics for contrast based visual saliency detection
    Xu X.
    Wang Y.-L.
    Zhang X.-L.
    Journal of Shanghai Jiaotong University (Science), 2015, 20 (01) : 14 - 20
  • [26] Exploiting Local and Global Characteristics for Contrast Based Visual Saliency Detection
    徐新
    王英林
    张晓龙
    Journal of Shanghai Jiaotong University(Science), 2015, 20 (01) : 14 - 20
  • [27] A Saliency Detection Model Based on Local and Global Kernel Density Estimation
    Jing, Huiyun
    He, Xin
    Han, Qi
    Niu, Xiamu
    NEURAL INFORMATION PROCESSING, PT I, 2011, 7062 : 164 - +
  • [28] Deep Network Saliency Detection Based on Global Model and Local Optimization
    Liu F.
    Shen T.
    Lou S.
    Han B.
    Guangxue Xuebao/Acta Optica Sinica, 2017, 37 (12):
  • [29] Saliency detection of infrared image based on region covariance and global feature
    Liu Songtao
    Jiang Ning
    Liu Zhenxing
    Jiang Kanghui
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2018, 29 (03) : 483 - 490
  • [30] Saliency detection of infrared image based on region covariance and global feature
    LIU Songtao
    JIANG Ning
    LIU Zhenxing
    JIANG Kanghui
    Journal of Systems Engineering and Electronics, 2018, 29 (03) : 483 - 490