Efficient Texture Classification Using a Kohonen Clustering Network and the LNLBP Attributes

In this paper, a Kohonen clustering network is proposed for efficient texture classification. Our goal is to be able to determine with accuracy different classes of similar and superposed textures. To this end, we introduce a new concept of local binary patterns called large neighborhoods local binary pattern (LNLBP), for discriminative network classification. The processed pixel to be classified considers window of large neighborhoods perversely to classic techniques that consider small sized windows. In addition, the use of characterizing parameters and a study for optimal windows size selection are proposed. A database composed by image holding similar textures patterns is used. The proposed approach generates classification results with high accuracy and reliability. A comparison study is conducted and proved that this approach is more efficient than many recent published methods.