SVM Spatial Pyramid Kernel with Lines and Ellipses Feature Hierarchy for Autonomous Classification of Similar Insect Species

This project focuses on automating the classification of 4700+ photographs from 29 insect species, developing ideas in computer vision and machine learning. Autonomous categorization aims at reducing the manual cost and the number of mistakes even when compared to experts. Therefore, to be competitive, improving accuracy is important. Challenges to accuracy in the 29-category dataset are present in the wide intra-class variations that impose the need for robust appearance and shape feature descriptors. We overcome the difficulties by stacking both descriptors, while preserving spatial information by using a spatial pyramid kernel for the support vector machine (SVM). This kernel achieved 88% overall accuracy, compared to 87% with a histogram intersection kernel, or 84.5% and more time-consuming with a RBF kernel. On the vision aspect, ongoing work attempts to produce a new shape descriptor by extracting medial axis line segments and ellipses from the image to form a paired hierarchy that describes relative shape, location, and angles in neighboring parts of an insect's body. The initial run of this descriptor alone obtained an accuracy of 66%. We hope to achieve even higher accuracies by improving the algorithm and combining this descriptor with other existing ones as described in [11].