Content-based Image Retrieval for Medicinal Plants Identification using Ensemble of SVM
In order to protect human health, it is very importance to know medicinal plant accurately. The plant identification is not easy and time-consuming task because of diversity. This paper proposes content-based image retrieval (CBIR) for medicinal plant identification using ensemble of Support Vector Machine (SVM). This research developed computerized geometric morphometric method to extract global shape descriptor. We investigated the combination of leaf texture and leaf geometry features based on Fuzzy Local Binary Pattern (FLBP) and global shape descriptor. Weighted Similarity-based Combination method was used to combine the features. We evaluated 660 medicinal leaves images of 66 species. The experiment results show that SVM ensemble of multiple features has good performance with the accuracy of classification is 0.93. CBIR system is developed for medicinal plant identification. The average precision of the CBIR system is 0.913. The proposed method is promising and potential to identify medicinal plant automatically.
CBIR, leaf identification, ensemble of SVM, FLBP, global shape descriptor