Sabanci-Okan system in LifeCLEF 2015 plant identification competition

Abstract

We present our deep learning based plant identification system in the LifeCLEF 2015. The approach is based on a simple deep convolutional network called PCANet and does not require large amounts of data due to using principal component analysis to learn the weights. After learning multistage filter banks, a simple binary hashing is applied to the filtered data, and features are pooled from block histograms. A multiclass linear support vector machine is then trained and the system is evaluated using the plant task datasets of LifeCLEF 2014 and 2015. As announced by the organizers, our submission achieved an overall inverse rank score of 0.153 in the image-based and an inverse rank score of 0.162 in the observation-based task of LifeCLEF 2015, as well as an inverse rank score of 0.51 for the LeafScan dataset of LifeCLEF 2014.