Automatic Representation and Classifier Optimization for Image-based Object Recognition

The development of image-based object recognition systems with the desired performance is – still – a challenging task even for experts. The properties of the object feature representation have a great impact on the performance of any machine learning algorithm. Manifold learning algorithms like e.g. PCA, Isomap or Autoencoders have the potential to automatically learn lower dimensional and more useful features. However, the interplay of features, classifiers and hyperparameters is complex and needs to be carefully tuned for each learning task which is very time-consuming, if it is done manually. This paper uses a holistic optimization framework with feature selection, multiple manifold learning algorithms, multiple classifier concepts and hyperparameter optimization to automatically generate pipelines for image-based object classification. An evolutionary algorithm is used to efficiently find suitable pipeline configurations for each learning task. Experiments show the effectiveness of the proposed representation and classifier tuning on several high-dimensional object recognition datasets. The proposed system outperforms other state-of-the-art optimization frameworks.