Wavelet-Based Salient Points: Applications to Image Retrieval Using Color and Texture Features
In image retrieval, global features related to color or texture are commonly used to describe the image. The use of interest points in contentbased image retrieval allows image index to represent local properties of images. Classic corner detectors can be used for this purpose. However, they have drawbacks when applied to various natural images for image retrieval, because visual features need not to be corners and corners may gather in small regions. We present a salient point detector that extracts points where variations occur in the image, regardless whether they are corner-like or not. It is based on wavelet transform to detect global variations as well as local ones. We show that extracting the color information in the locations given by these points provides significantly improved retrieval results as compared to the global color feature approach. We also show an image retrieval experiment based on texture features where our detector provides better retrieval performance comparing with other point detectors.
KeywordsImage Retrieval Wavelet Coefficient Interest Point Natural Image Salient Point
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- 1.S. Bhattacharjee and T._Ebrahimi, “Image Retrieval Based on Structural Content”, Workshop on Image Analysis for Multimedia Interactive Services, Heinrich-Hertz-Institut (HHI) Berlin, Germany, May 31-June 1 1999.Google Scholar
- 2.S. Bres and J.-M. Jolion, “Detection of Interest Points for Image Indexation”, 3rd Int. Conf. on Visual Information Systems, Visual99, Amsterdam, The Netherlands, June 2–4 1999, pp. 427–434.Google Scholar
- 6.C. Harris and M. Stephens, “A Combined Corner and Edge Detector”, Proc. of 4th Alvey Vision Conference, 1988, pp. 147–151.Google Scholar
- 7.E. Loupias and N. Sebe, “Wavelet-based Salient Points for Image Retrieval”, RR 99.11, Laboratoire RFV, INSA Lyon, November 1999. http://rfv.insa-lyon.fr/~loupias/points/
- 8.S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”, IEEE Trans. on PAMI, July 1989, Vol. 11, No. 7, pp. 674–693.Google Scholar
- 9.C. Schmid and R. Mohr, “Local Grayvalue Invariants for Image Retrieval”, IEEE Trans. on PAMI, May 1997, Vol. 19, No. 5, pp. 530–535.Google Scholar
- 10.M. Stricker and M. Orengo, “Similarity of Color Images”, SPIE-Storage and Retrieval for Image and Video Databases, 1995.Google Scholar
- 11.T. Tuytelaars and L. VanGool, “Content-based Image Retrieval Based on Local Affinely Invariant Regions”, 3rd Int. Conf. on Visual Information Systems, Visual99, Amsterdam, The Netherlands,-4 June 1999, pp. 493–500.Google Scholar
- 12.C. Wolf, J.-M. Jolion, W. Kropatsch and H. Bischof, “Content Based Image Retrieval Using Interest Points and Texture Features”, to appear in Proceedings of 15th ICPR, 2000.Google Scholar