Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

@article{Bianconi2017ImprovedOC,
  title={Improved opponent color local binary patterns: an effective local image descriptor for color texture classification},
  author={Francesco Bianconi and Raquel Bello-Cerezo and Paolo Napoletano},
  journal={Journal of Electronic Imaging},
  year={2017},
  volume={27}
}
Abstract. Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 10 CITATIONS

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

  • Sensors
  • 2019
VIEW 5 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Snapshot multispectral image demosaicing and classification

VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 64 REFERENCES

taxonomy and experimental study,” Pattern Recognit

L. Liu et al., “Local binary features for texture classification
  • 62, 135–160
  • 2017
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

a proposed benchmark suite for biological image analysis,” Med

L. Shamir et al., “IICBU 2008
  • Biol. Eng. Comput. 46(9), 943–947
  • 2008
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Tajeripour, “Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm,

F. S. Fekriershad
  • Sens. Rev
  • 2017

an experimental comparison,” in Intelligent Interactive Multimedia Systems and Services (KES-IIMSS 2017), Vilamoura, Portugal, Smart Innovation, Systems and Technologies, G

R. Bello-Cerezo et al., “Hand-designed local image descriptors vs. offthe-shelf CNN-based classification
  • De Pietro et al., Eds., Vol. 76
  • 2017