Bag-of-features (BOF) model is one of the computer vision-based method to classify image, and this model was derived from the bag-of-words model developed for automatic classification of documents. The BOF is simple and powerful, so it widely used for image classification. In this study, we tried to identify Lauraceae image database. The database including 1658 optical micrographs form 11 genera with 39 species. Lauraceae is known as a family that is difficult to identify because of its vast variety of species and similar features. It is the reason why we want to identify this family by the computer vison-based method. The image features were extracted using the scale-invariant feature transform (SIFT) algorithm, and the identification was performed by the support vector machine (SVM). To find the species-specific feature, we used tf-idf (term frequency - inverse document frequency) score weight. The score of common features are reduced, while score of rare, unique, and important features are increased. From the tf-idf score, therefore, we can find the species-specific features. The identification accuracy of the BOF was excellent at 98.2%. The accuracy is slightly higher than the 95.4% accuracy of our previous study that used only the SIFT algorithm and SVM without using the BOF. The tf-idf score showed important features for each species, and the features varied from species to species. Furthermore, the species-specific features differed by species in the same anatomical features and even showed area-specific characteristics within the same anatomical features of the same species. From the BOF model with the tf-idf score, we were able to better understand what the computer looks from the image.