Features Type

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

1. Features in the Pixel Domain (360 papers)

    1.1. Crisp Features (302 papers)

• • Intensity features (39 papers)

  • Color features (18 papers).

  • Color Features in Designed Shape Color Spaces (13 papers)

  • Designed Illumination Invariant Color Features (6 papers)

  • Color Filter Array (CFA) Features (1 paper)

  • Statistics on Color Features (10 papers)

  • Edge features (23 papers)

  • Texture features (145 papers)

  • Motion features (2 papers)

  • Stereo features (45 papers)

    1.2. Statistical Features (26 papers)

• • Histograms : Histogram of intensity (1 paper), Histograms of color space (HC) (9 papers), Histograms of Gradient (HG) (7 papers), Histograms of Figure/Ground Segmentation (HFG) (1 paper)

  • Correlograms

  • Entropy (4 papers)

  • Statistical Reach Feature (2 papers)

  • Statistical Texture (2 papers)

    1.3. Fuzzy Features (16 papers)

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

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

  • Fuzzy Histon (FH) (3 papers)

  • Fuzzy Correlogram (FC) (2 papers)

  • Fuzzy Statistical Texture (FST) (3 papers)

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

    1.4. Other Features  (16 papers)

• • Frequency Features (2 paperq)

  • Phase Features (5 papers)

  • Spatial Features (1 paper)

  • Location Features (7 papers)

  • Bit Plane Features (1 paper)

2. Features in a Transform Domain (32 papers)

• • Frequency Features (21 papers)

  • Video Features (11 papers)

3.  Features in Deep Learning Domain (17 papers)

• • Deep Learned Features (17 papers)

Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ.  Rochelle, France.

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

T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”, Computer Science Review, 2014. [pdf]

T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]

T. Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey", Recent Patents on Computer Science, Volume 4, No. 3, pages147-176, September 2011. [pdf]

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