Addressing Overfitting on Pointcloud Classification using Atrous XCRF
This addresses overfitting for pointcloud classification tasks, offering an incremental improvement in performance.
The paper tackles overfitting in pointcloud classification with limited labeled data by introducing Atrous XCRF, which applies conditional random field similarity penalties using unlabeled data, achieving 84.97% overall accuracy and 71.05% F1 score on the ISPRS 3D labeling dataset, matching the best model and setting a new high in F1 score.
Advances in techniques for automated classification of pointcloud data introduce great opportunities for many new and existing applications. However, with a limited number of labeled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 84.97% in term of overall accuracy, and 71.05% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score.