Abstract
Local descriptors have been wildly explored and utilized in image retrieval because of their transformation invariance. In this paper, we propose an improved set of features extarcted from local descriptors for more effective and efficient image retrieval. We propose a salient region selection method to detect human’s Region Of Interest (hROI) from an image, which incorporates the Canny edge algorithm and the convex hull method into Itti’s saliency model for obtaining hROI’s. Our approach is a purely bottom-up process with better robustness. The salient region is used as a window to select the most distinctive features out of the Scale-Invariant Feature Transform (SIFT) features. Our proposed SIFT local descriptors is termed as salient-SIFT features. Experiment results show that the salient-SIFT features can characterize the human perception well and achieve better image retrieval performance than the original SIFT descriptors while the computational complexity is greatly reduced.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zou, W., Chi, Z., Chuen Lo, K.: Improvement of Image Classification Using Wavelet Coefficients with Structured-Based Neural Network. International Journal of Neural Systems 18(3), 195–205 (2008)
Chi, Z., Yan, H.: Feature Evaluation and Selection Based on An Entropy Measurement with Data Clustering. Optical Engineering 34(12), 3514–3519 (1995)
Lazebnic, S., Schmid, C., Ponce, J.: Spare Texture Representation Using Affine-invariant Neighborhoods. In: Proceedings of Computer Vision and Pattern Recognition, pp. 319–324 (2003)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 2(60), 91–110 (2004)
Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. In: Proceedings of Computer Vision and Pattern Recognition, pp. 511–517 (2004)
Fu, H., Chi, Z., Feng, D.: Attention-Driven Image Interpretation with Application to Image Retrieval. Pattern Recognition 39(9), 1604–1621 (2006)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Gao, K., Lin, S.X., Zhang, Y.D., Tang, S., Ren, H.M.: Attention Model Based SIFT Keypoints Filtration for Image Retrieval. In: Seventh IEEE/ACIS International Conference on Computer and Information Science, pp. 191–196 (2007)
Koch, C., Ullman, S.: Shifts in Selective Visual Attention: Towards The Underlying Neural Circuitry. Human Neurobiology 4(4), 319–327 (1985)
Griffin, G., Holub, A.D., Perona, P.: The Caltech-256. Caltech Technical Report, 1–20 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liang, Z., Fu, H., Chi, Z., Feng, D. (2010). Salient-SIFT for Image Retrieval. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-17688-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17687-6
Online ISBN: 978-3-642-17688-3
eBook Packages: Computer ScienceComputer Science (R0)