Advertisement

An Approach to Detect an Image as a Selfie Using Object Recognition Methods

  • Madhuri A. Bhalekar
  • Mangesh V. Bedekar
  • Saba Aslam
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

Selfie is the act of taking self portrait through the front camera of the mobile. By visualizing the captured image one can identify the details regarding the image such as number of objects, location and much more. By the method of object recognition in the image we can identify whether the image taken is a selfie or not. For this we should first segregate both the foreground and background details from an image. From the details of foreground one can identify the object (i.e., the person taking the selfie) and from the background we can tell about the location. Using various object recognition methods such as exhaustive search, segmentation, selective search, Gaussian mixture model the information regarding objects, foreground and background can be detected. And further the value of foreground and background will be compared with a certain threshold value and according to the obtained result can recognized whether an image is a selfie or not. In this paper we are presenting an approach which can be used to detect an image is a selfie.

Keywords

Object recognition Selfie Exhaustive search Segmentation Selective search Gaussian mixture model 

References

  1. 1.
    Orekh, E., Sergeyeva, O., Bogomiagkova, E.: Selfie phenomenon in the visual content of social media. IEEE Conference (2016)Google Scholar
  2. 2.
    Du Preez, A.: Sublime selfies: to witness death. Eur. J. Cult. Stud. Published 10 August (2017)Google Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  4. 4.
    Harzallah, H., Jurise, F., Schmid, C.: Combining efficient object localization and image classification. In: ICCV (2009)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. CVPR 1, 511–518 (2001)Google Scholar
  6. 6.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  7. 7.
    Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR (2010)Google Scholar
  8. 8.
    Uijlings, J.R., Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vision 104(2), 154–171 (2013)CrossRefGoogle Scholar
  9. 9.
    Anghelescu, P., Iliescu, V.G., Mara, C., Gavriloaia, M.B.: Automatic thresholding method for edge detection algorithms. In: ECAI—International Conference, 8th edn (2016)Google Scholar
  10. 10.
    Kim, S.J., Kim, B.S., Kim, H.I., Hong, T.H., Son, J.Y.: The method for defocusing selfie taken by mobile frontal camera using burst shot. IEEE Conference (2016)Google Scholar
  11. 11.
    Kamate, S., Yilmazer, N.: Application of object detection and tracking techniques forunmanned aerial vehicles. Published by Elsevier, Science Direct (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Madhuri A. Bhalekar
    • 1
  • Mangesh V. Bedekar
    • 1
  • Saba Aslam
    • 1
  1. 1.Department of Computer EngineeringMAEER’S Maharashtra Institute of Technology, Savitribai Phule Pune UniversityPuneIndia

Personalised recommendations