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]:
This paper compares the use of hand-designed and learned features for the analysis of 3d objects. The authors use an extensive set of 25 hand-designed features to obtain 92.33% accuracy for the task of agglomerating neuron fragments. They then explored fully supervised end to end learning of features from raw inputs, but only obtain 85.54% accuracy. However, because the data is small compared to its dimensionality, unsupervised learning provides some improvement. Finally, a dynamic pooling method allows them to match the hand-designed features score. Data augmentation however brings fully supervised and unsupervised approaches on par.

Novelty and quality: I am not very familiar with the literature in 3d analysis but the introduction suggests that feature learning (fully supervised and unsupervised) has not been used before or is not common. If so, the methods themselves are not novel but their application to this particular field is. It would be good if the authors could state clearly if this has ever been tried before or not. The quality of the work is good and thorough.

Pros:
- directly comparing hand-designed and learned features is good.
- learned features are shown to be slightly superior in accuracy.