Center Symmetric Local Descriptors for Image Classification

Literature Review

We present here a brief review of the various methods for texture classification that are proposed in the literature. Ojala, Pietikainen and Harwood (1996) introduced the Local Binary Pattern (LBP) as an approach for texture classification. Owing to its success in the texture classification task, the LBP operator then finds its way into various classification problems such as facial recognition as proposed in Ahonen, Hadid and Pietikainen (2006). Rotation invariance is something the LBP operator does not possess which is a disadvantage of this operator. Thus, in Ojala, Pietikainen, and Maenpaa (2002) a rotation invariant extension of LBP was introduced. Later it was found out that LBP was sensitive to global intensity variations and also to local intensity along edge components. To handle these shortcomings in Jun and Kim (2012) a new method was introduced called Local Gradient Pattern (LGP). LGP was shown to have higher discriminant power than LBP in the case of facial recognition.