Features in the Pixel Domain

There are several features in the pixel domain that are used in the literature and they can be classified in the following categories:

1. Crisp Features (302 papers)

• • Intensity features (39 papers)

  • Color features (48 papers)

  • Edge features (23 papers)

  • Texture features (145 papers)

  • Motion features (2 papers)

  • Stereo features (45 papers)

2. Statistical Features (26 papers)

• • Histogram : Histogram of intensity (1 paper), Histograms of color space (HC) (9 papers), Histograms of Gradient (HG) (7 paper),

                            Histograms of Figure/Ground Segmentations (HFG) (1 paper)

• • Correlogram

  • Entropy (4 papers)

  • Statistical Reach Features (2 papers)

  • Statistical Texture (2 papers)

3. Fuzzy Features (15 papers)

• • Local Fuzzy Color Histograms (LFCH) (6 papers)                       

  • Local Fuzzy Color Difference Histograms (LFCDH) (1 paper) 

  • Fuzzy Histon (FH) (2 papers)

  • Fuzzy Correlogram (FC) (2 papers)

  • Fuzzy Statistical Texture (FST) (3 papers)

  • Fuzzy Local Binary Pattern (FLBP) (1 paper)

4. Other Features (16 papers)

• • Frequency Features (2 paperq)

  • Phase Features (5 papers)

  • Spatial Features (1 paper)

  • Location Features (7 papers)

  • Bit Planes eatures (1 paper)

Fair Use Policy

As this website gives many information that come from my research, please cite my following papers:

 C. Silva, T. Bouwmans, C. Frelicot, 'An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos", VISAPP 2015, Berlin, Germany, March 2015.

C. Silva, T.  Bouwmans, C. Frelicot, "Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation", International Conference on  Pattern Recognition, ICPR 2016, December 2016.

C. Pacheco, T. Bouwmans, C. Frelicot,"Superpixel-based online wagging one-class  ensemble for feature selection in foreground/background separation", Pattern Recognition Letters, 2017. 

T. Bouwmans, C. Silva, C. Marghes, M. Zitouni, H. Bhaskar, C. Frelicot, “On the Role and the Importance of Features for Background Modeling and Foreground Detection”, Computer Science Review, Volume 28, pages 26-91, May 2018.

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