Abstract
The paper presents a contour-based method for large scale image retrieval. With the contour saliency map of the object, it could address the shift-invariance problem, and with hierarchical and multi-scale feature extraction, it is able to deal with the scale-invariance problem to a certain extent. Different from existing algorithms, the features used in the retrieval system contain not only local information, but also global information of the object. By taking advantage of this characteristic, we could build a hierarchical index structure which helps to fast retrieval of the large scale database. Furthermore, our method allows two kinds of query image: a hand-drawn sketch or a natural image. Thus it is possible to refine the search results by choosing one image from the list of previous sketch retrieval results as the new query. It brings the better interactive user experiment and the convenience for those who aren’t good at drawing. The experiment results verify the performance of our method on a database of four million images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2), 1–60 (2008)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Context. IEEE Trans. PAMI 24(4), 509–522 (2002)
Tieu, K., Viola, P.: Boosting Image Retrieval. IJCV 56(1/2), 17–36 (2004)
Shechtman, E., Irani, M.: Matching Local Self-Similarities across Images and Videos. In: CVPR, pp. 1–8 (2007)
Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: An Evaluation of Descriptors for Large-Scale Image Retrieval from Sketched Feature Lines. Computers & Graphics 34, 482–498 (2010)
Cao, Y., Wang, C.H., Zhang, L.Q., Zhang, L.: Edgel Index for Large-Scale Sketch-based Image Search. In: CVPR (accepted, 2011)
Torralba, A., Fergus, R., Freeman, W.T.: 80 Million Tiny Images: A Large Dataset for Non-Parametric Object and Scene Recognition. IEEE Trans. PAMI 30(11), 1958–1970 (2008)
Canny, J.: A computational Approach to Edge Detection. IEEE Trans. PAMI 8(6), 679–698 (1986)
Gao, D., Mahadevan, V., Vasconcelos, N.: On the Plausibility of the Discriminant Center-Surround Hypothesis for Visual Saliency. Journal of Vision 8(7), 13, 1–18 (2008)
Seo, H.J., Milanfar, P.: Static and Space-Time Visual Saliency Detection by Self-Resemblance. Journal of Vision 9(12), 15, 1–27 (2009)
Bruce, N.D.B., Tsotsos, J.K.: Saliency Based on Information Maximization. In: NIPS, vol. 18, pp. 155–162 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, R., Zhang, L. (2011). Contour-Based Large Scale Image Retrieval. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-24965-5_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
eBook Packages: Computer ScienceComputer Science (R0)