The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Boosting color saliency in image feature detection
Open accessAbstract
The aim of salient feature detection is to find distinctive local events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape-saliency of the local neighborhood. The majority of these detectors are luminance-based, which has the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. In this paper, color distinctiveness is explicitly incorporated into the design of saliency detection. The algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
Cited by
Bayesian Saliency via Low and Mid Level Cues
Xie, Y., Lu, H., & Yang, M.-H. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2216276 · Cited by 367
Metadata
- Authors
- van de Weijer, J., Gevers, T., & Bagdanov, A. D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-01
- Volume
- 28
- Issue
- 1
- Pages
- 150-156
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2120504013
- Cited by
- 301
- References
- 4
- FWCI
- 13.72
- Type
- journal article
- Language
- en
- Open access
- green
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