Abstract
The feature extraction is the most critical step in image retrieval. Among various local feature extraction methods, scale-invariant feature transform (SIFT) has been proven to be the most robust local invariant feature descriptor, which is widely used in the field of image matching and retrieval. However, the SIFT algorithm has a disadvantage that the algorithm will produce a large number of feature points and is not suited for widely using in the field of image retrieval. Firstly, a novel significant measure algorithm is proposed in this paper, and the regions of interest in images are obtained. Then, SIFT features are extracted from salient regions, reducing the number of SIFT features. Our algorithm also abstracts color features from salient regions, and this method overcomes SIFT algorithm’s drawback that could not reflect image’s color information. The experiments demonstrate that the integrated visual saliency analysis-based feature selection algorithm provides significant benefits both in retrieval accuracy and in speed.
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
References
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE conference on computer vision and pattern recognition, pp 1–8
Cheng MM, Zhang GX, Mitra NJ et al (2011) Global contrast based salient region detection. In: IEEE conference on computer vision and pattern recognition, pp 409–416
Goferman S, Zelnik-Manor L, Ayellet T et al (2012) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vision 7(1):11–32
Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features-based on Gabor filters. IEEE Trans Image Process 11(19):1160–1167
Stottinger J et al (2012) Sparse color interest points for image retrieval and object categorization. IEEE Trans Image Process 21(5):2681–2692
Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the eleventh ACM international conference on Multimedia. ACM, pp 374–381
Liu GH, Li ZY, Zhang L et al (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133
Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: Proceedings of ninth IEEE international conference on computer vision. IEEE, pp 1470–1477
Acknowledgment
The authors would like to thank our anonymous reviewers for their valuable comments. This work was supported in part by grants from National Natural Science Foundation of China (nos. 61303101, 61170326, and 61170077), the Natural Science Foundation of Guangdong Province, China (no. S2012040008028 and S2013010012555), the Shenzhen Research Foundation for Basic Research, China (nos. JCYJ20120613170718514, JCYJ20130326112201234, JC201005250052A, JC20130325014346, JCYJ20130329102051856, and ZD201010250104A), the Shenzhen Peacock Plan (no. KQCX20130621101205783), the Start-up Research Foundation of Shenzhen University (no. 2012-801, 2013-000009), and Shenzhen Nanshan District entrepreneurship research (308298210022).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wen, Z., Gao, J., Luo, R., Wu, H. (2014). Image Retrieval Based on Saliency Attention. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-54924-3_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54923-6
Online ISBN: 978-3-642-54924-3
eBook Packages: EngineeringEngineering (R0)