An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals

By Emmanuel Okafor, Lambert Schomaker, Marco A. Wiering. 2018

35 citations recorded by the source corpus.

ABSTRACT In this paper, we examine a novel data augmentation (DA) method that transforms an image into a new image containing multiple rotated copies of the original image. The DA method creates a grid of cells, in which each cell contains a different randomly rotated image and introduces a natural background in the newly created image. We investigate the use of deep learning to assess the classification performance on the rotation matrix or original dataset with colour constancy versions of the datasets. For the colour constancy methods, we use two well-known retinex techniques: the multi-scale retinex and the multi-scale retinex with colour restoration for enhancing both original (ORIG) and rotation matrix (ROT) images. We perform experiments on three datasets containing images of animals, from which the first dataset is collected by us and contains aerial images of cows or non-cow backgrounds. To classify the Aerial UAV images, we use a convolutional neural network (CNN) architecture and compare two loss functions (hinge loss and cross-entropy loss). Additionally, we compare the CNN to classical feature-based techniques combined with a k-nearest neighbour classifier or a support vector machine. The best approach is then used to examine the colour constancy DA variants, ORIG and ROT-DA alone for three datasets (Aerial UAV, Bird-600 and Croatia fish). The results show that the rotation matrix data augmentation is very helpful for the Aerial UAV dataset. Furthermore, the colour constancy data augmentation is helpful for the Bird-600 dataset. Finally, the results show that the fine-tuned CNNs significantly outperform the CNNs trained from scratch on the Croatia fish and the Bird-600 datasets, and obtain very high accuracies on the Aerial UAV and Bird-600 datasets.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (2019, background): “Some additional approaches to generate augmented data include image combining (Inoue, 2018; Okafor et al., 2018), elastic distortions (Wong et al., 2016), and generative adversarial networks (Antoniou et al., 2017).”
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness (2019, background): “Approaches range from elastic transformations [2], noise generation in a learned features space [3], to repeat, rotate and inﬁll approaches whereby a known sample is scaled and rotated in a grid pattern, and background consistency is ensured [4].”