Color Invariance

By Jan-Mark Geusebroek, R. V. D. Boomgaard, Arnold M. W. Smeulders, H. Geerts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001

646 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/281b195c5b155c461d679444c3e2aeb06aa7e351

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Cees G. M. Snoek
2. Gertjan J. Burghouts
3. Matthew A. Brown
4. Sabine Süsstrunk
5. Jan-Mark Geusebroek

In Computer Vision and Pattern Recognition

1. Cees G. M. Snoek
2. Marcel Worring
3. Sabine Süsstrunk
4. Gertjan J. Burghouts
5. Matthew A. Brown
6. Jan-Mark Geusebroek
7. Luc Van Gool
8. Dacheng Tao

In Media Technology

1. Matthew A. Brown
2. Sabine Süsstrunk
3. Gertjan J. Burghouts
4. Marcel Worring
5. Jan-Mark Geusebroek
6. Cees G. M. Snoek
7. Christian Beecks
8. Mubarak Shah

Citations in the corpus

• Visual Tracking: an Experimental Survey (2013, neutral): “Color invariants [81] reduce the influence of the fluctuations in the illumination, which play an important role in tracking [62], for which fast implementations [82], [83] exist.”
• A Feature-Enriched Completely Blind Image Quality Evaluator (2015, neutral): “To exploit the expression of distortions in image color space, we also transform RGB images into a perceptually relevant opponent color space [36] prior to computing the NSS gradient features: ⎡ ⎣ O 1 O 2 O 3 ⎤ ⎦ = ⎡ ⎣ 0.06 0.63 0.27 0.30 0.04 −0.35 0.34 −0.6 0.17 ⎤ ⎦ ⎡ ⎣ R G B ⎤ ⎦ (9) The weights…”
• Concept-Based Video Retrieval (2009, background): “There are numerous color spaces to choose from in which to compute the numerical color values per pixel, including standard RGB, the intuitive HSV space, the perceptually uniform L * a * b * space, or the invariant sets of color spaces in [63] [66].”
• Coloring Local Feature Extraction (2006, critical): “This problem is usually overlooked in derivative-based invariance literature [7], [10], [20].”
• Performance evaluation of local colour invariants (2009, positive): “We consider for 1000 objects from the ALOI database [25], the following imaging conditions: JPEG compression, blurring, and changes of the viewpoint, illumination direction and illumination colour.”
• Multi-spectral SIFT for scene category recognition (2011, methodology): “We extensively test our algorithms using a new dataset of several hundred RGB-NIR scene images, as well as benchmarking against Torralba’s scene categorization dataset.”