Viewpoints Combined Classification Method in Image-based Plant Identification Task

The image-based plant identification challenge was focused on tree, herbs and ferns species identification based on different types of images. The aim of the task was to produce relevant species for each observation of a plant of the test dataset. We have elaborated a viewpoints combined classification method for this challenge. We have applied dense SIFT for feature detection and description; and Gaussian Mixture Model based Fisher vector was calculated to represent an image with high-level descriptor. The chosen classifier was the C-support vector classification algorithm with RBF (Radial Basis Function) kernel, and we have optimized two hyperparameters (C from C-SVC and γ from RBF kernel) by a grid search with two-dimensional grid. We have constructed a combined classifier using the weighted average of reliability values of classifier at each viewpoint. The results show that our combined method exceeds our best classifier among the list of classifiers constructed for different viewpoints.