Abstract
Visual attention model was usually used for salient region detection. However, little work has been employed to use the model for salient edge extraction. Since edge information is also important element to represent the semantic content of an image, in this paper, attention model is extended for salient edges detection. In our approach, an improved saliency map computing algorithm is employed first. Then, based on the saliency map, a novel and efficient salient edges detection method is introduced. Moreover, the concept of salient edge histogram descriptors (SEHDs) is proposed for image similarity comparison. Experiments show that the proposed algorithm works well.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Datta, R., Li, J., Wang, J.Z.: Content-Based Image Retrieval - Approaches and Trends of the New Age. In: ACM MIR (2005)
Hu, Y., Xie, X., Ma, W.Y., Rajan, D., Chia, L.T.: Salient object extraction combining visual attention and edge information. Technical Report (2004)
Wang, S., Kubota, T., Siskind, J.M., Wang, J.: Salient Closed Boundary Extraction with Ratio Contour. IEEE Trans on Pattern Analysis and Machine Intelligence 27(4), 546–561 (2005)
Elder, J., Zucker, S.: Computing contour closure. In: European Conference on Computer Vision, pp. 399–412 (1996)
Zhou, X.S., Huang, T.S.: Edge-Based Structural Features for Content-Based Image Retrieval. Pattern Recognition Letters 22(5), 457–468 (2001)
Feng, S.H., Xu, D.: A Novel Region-Based Image Retrieval Algorithm Using Selective Visual Attention Model. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 235–242. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Songhe, F., De, X. (2006). Locating Salient Edges for CBIR Based on Visual Attention Model. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_38
Download citation
DOI: https://doi.org/10.1007/11881070_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)