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Journal of Real-Time Image Processing

, Volume 16, Issue 1, pp 227–240 | Cite as

Partially shaded sketch-based image search in real mobile device environments via sketch-oriented compact neural codes

  • Jamil Ahmad
  • Khan Muhammad
  • Syed Inayat Ali Shah
  • Arun Kumar Sangaiah
  • Sung Wook BaikEmail author
Special Issue Paper
  • 271 Downloads

Abstract

With the advent of touch screens in mobile devices, sketch-based image search is becoming the most intuitive method to query multimedia contents. Traditionally, sketch-based queries were formulated with hand-drawn shapes without any shades or colors. The absence of such critical information from sketches increased the ambiguity between natural images and their sketches. Although it was previously considered too cumbersome for users to add colors to hand-drawn sketches in image retrieval systems, the modern day touch input devices make it convenient to add shades or colors to query sketches. In this work, we propose deep neural codes extracted from partially colored sketches by an efficient convolutional neural network (CNN) fine-tuned on sketch-oriented augmented dataset. The training dataset is constructed with hand-drawn sketches, natural color images, de-colorized, and de-texturized images, coarse and fine edge maps, and flipped and rotated images. Fine-tuning CNN with augmented dataset enabled it to capture features effectively for representing partially colored sketches. We also studied the effects of shading and partial coloring on retrieval performance and show that the proposed method provides superior performance in sketch-based large-scale image retrieval on mobile devices as compared to other state-of-the-art methods.

Keywords

Sketch-based query Image retrieval Hash codes Deep learning Convolutional neural network 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant Funded by the Korea Government (MSIP) (No. 2016R1A2B4011712).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jamil Ahmad
    • 1
  • Khan Muhammad
    • 2
  • Syed Inayat Ali Shah
    • 3
  • Arun Kumar Sangaiah
    • 4
  • Sung Wook Baik
    • 2
    Email author
  1. 1.Department of Computer ScienceIslamia CollegePeshawarPakistan
  2. 2.Digital Contents Research InstituteSejong UniversitySeoulSouth Korea
  3. 3.Department of MathematicsIslamia College PeshawarPeshawarPakistan
  4. 4.School of Computing Science and EngineeringVIT UniversityVelloreIndia

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