Learned versus Hand-Designed Feature Representations for 3d Agglomeration
Authors: John A. Bogovic, Gary B. Huang, Viren Jain
ICLR 2014

Abstract: For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy.


Peer review:
[Review]:
Thank you for the comments and suggestions.

We have avoided using the word 'significant' where inappropriate in our revision.

At this stage we have decided to leave the figures colored as they are, especially since only a subset of accepted papers will be published in the JMLR special topics issue. If this work is among those selected, we will revisit the figure appearance. Thank you once again for your suggestions.