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Robust Sketch-Based Image Retrieval by Saliency Detection

  • Xiao Zhang
  • Xuejin ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9516)

Abstract

Sketch-based image retrieval (SBIR) has been extensively studied for decades because sketch is one of the most intuitive ways to describe ideas. However, the large expressional gap between hand-drawn sketches and natural images with small-scale complex structures is the fundamental challenge for SBIR systems. We present a novel framework to efficiently retrieve images with a query sketch based on saliency detection. In order to extract primary contours of the scene and depress textures, a hierarchical saliency map is computed for each image. Object contours are extracted from the saliency map instead of the original natural image. Histograms of oriented gradients (HOG) are extracted at multiple scales on a dense gradient field. Using a bag-of-visual-words representation and an inverted index structure, our system efficiently retrieves images by sketches. The experimental results conducted on a dataset of 15 k photographs demonstrate that our method performs well for a wide range of natural scenes.

Keywords

Sketch Image retrieval Saliency Multi-scale 

Notes

Acknowledgments

We would like to thank the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (NSFC) under Nos. 61472377 and 61331017, and the Fundamental Research Funds for the Central Universities under No. WK2100060011.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application SystemUniversity of Science and Technology of ChinaHefeiChina

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