Fusion of Local Binary Patterns features for tropical medicinal plants identification

Abstract

Selection of leaf features that are appropriate for identification is very important. Local Binary Patterns (LBP) is one of texture feature that is efficiency and robustness for plant identification. Meanwhile, in LBP we have to define good size of sampling point. In this research we propose fusion of LBP features, which incorporates different size of sampling point. There are two ways for fusion of LBP. First, we perform a straightforward fusion by calculating histogram of multiple LBP features separately using varying the size of sampling points and radius, then concatenate the multiple histograms together. Second, each histogram of LBP features is classified, and the feature fusion can be accomplished by classifier combination. For leaf classification, we used probabilistic neural network (PNN) to classify LBP features. The experiment performed on tropical medicinal plants and house plants. According to experimental results, the fusion of LBP features can improve accuracy in plant identification. This system is very promising to help people identify medicinal plant automatically and for conservation and utilization of medicinal plants.