BibTeX:

@article{IJIRSTV3I4135,
     title={Texture Classification using Random Forest Classifier Based on Local Vector Quantization Pattern},
     author={Anumol Antony and Dr. Arun Kumar M N},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={4},
     pages={342--351},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I4135.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The Nearest Neighbor (NN) Classifier is the fundamental and simplest algorithm used for classification. It has successfully been used in classification and pattern recognition applications, such as texture recognition. However, it has a disadvantage that it has lower predictive accuracy. In order to overcome these disadvantages, we propose a new classification method using random forest classification based on local vector quantization pattern (LVQP). In LVQP, different kinds of texture images are chosen to train a local pattern codebook, where each different structural pattern is described by a unique codeword index. Contrarily to the original LBP and its many variants, LVQP does not quantize each neighborhood pixel separately to 0/1, but aims at quantizing the whole difference vector between the central pixel and its neighborhood pixels. Since LVQP deals with the structural pattern as a whole, it has a high discriminability and is less sensitive to noise.

Nearest Neighbor Classifier, Pattern Recognition, Texture Recognition, Random Forest, Local Vector Quantization Pattern