Bonsai Style Classification: a new database and baseline results

Abstract

Bonsai consists of an ancient art which is aimed at mimicking a tree in miniature. Despite being original and popular on the Asian continent, Bonsai has been widespread in several parts of the world. There are many techniques for styling the plants, classifying them in different patterns widely known by people who appreciate this art. In this work, we introduce a new database specially created for the development of research on Bonsai styles classification. The database is composed of 700 samples, equally distributed among the seven following classes: Formal Upright, Informal Upright, Slanting, Cascade, Semi Cascade, Literati and Wind Swept. The classes selected to compose the database were chosen considering the five basic styles and two more styles that have distinct characteristics from the others. The database was created by the authors themselves, using images available on the Pinterest platform, and they were subjected to a pre-processing criteria to remove similar photos and resize them. The baseline results presented here were obtained using deep models (CNN architectures) successfully used to address image classification tasks in different application domains: VGG, Xception, DenseNet and InceptionV3. These models were trained on ImageNet and we used transfer learning aiming to adapt it to the current proposal. In order to avoid overfitting, data augmentation was performed during training, along with the dropout method. Experimental results showed that VGG19 model obtained the highest accuracy rate, reaching 89%. In addition, we used DeconvNet and Deep Taylor methods aiming to find a proper explanation for the obtained results. It was noted that the VGG19 model better captured the most important aspects for the classification task investigated here, with a better performance to discriminate and generalize patterns in the task of classifying Bonsai styles.