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Indoor scene segmentation algorithm based on full convolutional neural network

  • S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
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Abstract

With the leaps and bounds of computer performance and the advent of the era of big data, deep learning has drawn more and more attention from all walks of life. It can combine low-level features to form more abstract high-level features and describe the data more essentially. Therefore, it is widely used in various fields such as computer vision. Image segmentation is one of the most basic research topics in the field of computer vision. The main purpose is to extract regions of interest from images for later image processing research. In 3D reconstruction based on sequence images, the segmentation accuracy and speed of sequence images determine the quality and efficiency of target reconstruction. Therefore, when facing large-scale sequence images, the biggest problem is how to improve the segmentation speed while ensuring accuracy. Based on the above background, the research content of this article is an indoor scene segmentation algorithm based on full convolutional neural network. According to the characteristics of indoor application scenes, this paper proposes a fast convolutional neural network image segmentation method to segment the indoor scene image and construct the fast fully convolutional networks (FFCN) for indoor scene image segmentation uses inter-layer fusion to reduce the amount of network calculation parameters and avoid the loss of picture feature information by continuous convolution. In order to verify the effectiveness of the network, in this paper, a basic living object data set (XAUT data set) in an indoor environment is created. The XAUT data set is used to train the FFCN network under the Caffe framework to obtain an indoor scene segmentation model. In order to compare the effectiveness of the model, the structure of the worn FCN8s, FCN16s, and FCN32s models was fine-tuned, and the corresponding algorithm model for indoor scene segmentation was obtained by training with the XAUT data set. The experimental results show that the pixel recognition accuracy of all types of networks has reached 86%, and the mean IU ratio has reached more than 63%. The mean IU of the FCN8s network is the highest at 70.38%, but its segmentation speed is only 1/5 of FFCN. On the premise that other types of indicators are not much different, the average segmentation speed on FFCN fast segmentation convolutional neural network reaches 40 fps. It can be seen that the scale fusion technology can well avoid the loss of image feature information in the network convolution and reddening process. Compared with other FCN networks, it has a faster speed and is conducive to real-time image preprocessing.

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References

  1. Dong X, Shen J, Shao L et al (2016) SubMarkov random walk for image segmentation. IEEE Trans Image Process 25(2):516–527

    Article  MathSciNet  Google Scholar 

  2. Zhu F, Bosch M, Khanna N et al (2015) Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE J Biomed Health Inf 19(1):377–388

    Article  Google Scholar 

  3. Chen LC, Papandreou G, Kokkinos I et al (2016) DeepLab: semantic Image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  4. Zhu H, Meng F, Cai J et al (2015) Beyond pixels: a comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. J Vis Commun Image Represent 34(2):12–27

    Google Scholar 

  5. Pont-Tuset J, Marques F (2015) Supervised evaluation of image segmentation and object proposal techniques. IEEE Trans Pattern Anal Mach Intell 38(7):1

    Google Scholar 

  6. Sridevi M, Mala C (2019) Self-organizing neural networks for image segmentation based on multiphase active contour. Neural Comput Appl 31:865–876

    Article  Google Scholar 

  7. Jiazi C, Aiguo S (2015) Research on the texture image segmentation method based on Markov random field. Chin J Sci Instrum 36(4):776–786

    Google Scholar 

  8. Deng C, Li S, Bian F et al (2015) Remote sensing image segmentation based on mean shift algorithm with adaptive bandwidth[J]. Commun Comput Inf Sci 482:179–185

    Google Scholar 

  9. Zhang SMBH (2015) A comparison of stochastic optimization techniques for image segmentation. Int J Intell Syst 15(5):441–476

    MATH  Google Scholar 

  10. Liang Y, Zhang M, Browne WN (2019) Figure-ground image segmentation using feature-based multi-objective genetic programming techniques. Neural Comput Appl 31:3075–3094

    Article  Google Scholar 

  11. Zeng T (2016) Residual deconvolutional networks for brain electron microscopy image segmentation. IEEE Trans Med Imaging 99:447–456

    Google Scholar 

  12. Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1D convolutional neural networks. IEEE Trans Bio-med Eng 63(3):664–675

    Article  Google Scholar 

  13. Saito S, Yamashita T, Aoki Y (2016) Multiple object extraction from aerial imagery with convolutional neural networks. Electron Imag 60(1):10402-1–10402-9

    Google Scholar 

  14. Zhao K, He D (2015) Recognition of individual dairy cattle based on convolutional neural networks. Nongye Gongcheng Xuebao/Trans Chin Soc Agric Eng 31(5):181–187

    Google Scholar 

  15. Xu Z, Mei L, Lv Z, Hu C, Luo X, Zhang H, Liu Y (2019) Multi-modal description of public safety events using surveillance and social media. IEEE Trans Big Data 5(4):529–539

    Article  Google Scholar 

  16. He T, Huang W, Qiao Y et al (2015) Text-attentional convolutional neural networks for scene text detection. IEEE Trans Image Process Publ IEEE Signal Process Soc 25(6):2529

    Article  MathSciNet  Google Scholar 

  17. Hou Y, Li Z, Wang P et al (2018) Skeleton optical spectra-based action recognition using convolutional neural networks. IEEE Trans Circuits Syst Video Technol 28(3):807–811

    Article  Google Scholar 

  18. Bondi L, Baroffio L, Güera D et al (2016) First steps toward camera model identification with convolutional neural networks. IEEE Signal Process Lett 24(3):259–263

    Article  Google Scholar 

  19. Nogueira RF, Lotufo RDA, Machado RC (2017) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11(6):1206–1213

    Article  Google Scholar 

  20. Dong Z, Wu Y, Pei M et al (2015) Vehicle type classification using unsupervised convolutional neural network. IEEE Trans Intell Transp Syst 16(4):1–10

    Article  Google Scholar 

  21. Poudel RPK, Lamata P, Montana G (2016) Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. Lect Notes Comput Sci 3824(1):164–173

    Google Scholar 

  22. Bell S, Bala K (2015) Learning visual similarity for product design with convolutional neural networks. ACM Trans Graph 34(4):98:1–98:10

    Article  Google Scholar 

  23. Garbin D, Vianello E, Bichler O et al (2015) HfO2-based OxRAM devices as synapses for convolutional neural networks. IEEE Trans Electron Dev 62(8):2494–2501

    Article  Google Scholar 

  24. Castelluccio M, Poggi G, Sansone C et al (2015) Land use classification in remote sensing images by convolutional neural networks. Acta Ecol Sin 28(2):627–635

    Google Scholar 

  25. Zhou W, Newsam S, Li C et al (2016) Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sensing 9(5):489

    Article  Google Scholar 

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Acknowledgements

This work was supported by a grant from the Characteristics innovation project of colleges and universities of Guangdong Province (Natural Science, No.  2019KTSCX235, 2019). This work was supported by 2017 of Guangxi Middle-aged and young teachers’ basic ability promotion project (No. 2017KY0073). This work was supported by 2019 of Guilin science research and technology development project (No. 20190211-23).

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Authors and Affiliations

  1. School of Information Science and Technology, South China Business College of Guangdong, University of Foreign Studies, Guangzhou, 510545, Guangdong, People’s Republic of China

    Zijiang Zhu, Yi Hu, Junshan Li, Dong Liu & Jianjun Li

  2. Institute of Intelligent Information Processing, South China Business College of Guangdong, University of Foreign Studies, Guangzhou, 510545, Guangdong, People’s Republic of China

    Zijiang Zhu & Junshan Li

  3. College of Physical Science and Technology, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Deming Li

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  1. Zijiang Zhu
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  3. Yi Hu
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  5. Dong Liu
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Correspondence to Deming Li.

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Zhu, Z., Li, D., Hu, Y. et al. Indoor scene segmentation algorithm based on full convolutional neural network. Neural Comput & Applic 33, 8261–8273 (2021). https://doi.org/10.1007/s00521-020-04961-0

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