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 works presents experiments on different features for the task of classifying agglomeration of 3d neuron fragments. Results of experiments using a wide range hand-crafted features and their combinations, as well as learned features are presented. The results shows that learned features can obtain performance similar to hand-crafted features, and that their combination can yield even better performance.

Novelty:
I am not very familiar with the field of 3d imaging, so it is difficult for me to asses the novelty of this work. But, I trust the authors when they claim that showing that learn features can perform as well as hand-crafted feature for analysis or classification of 3d shapes is a new result. Other contributions include evaluating performance of a large set of hand-crafted features, proposing an end-to-end approach to derive features, and augmenting the 3d images dataset with transformations.

Quality:
I found that the paper is well written and well structured. The experimental method is sound and the conclusions are in line with the results.

I recommend to accept this paper.

Pros:
 - Experiments are well constructed.
 - Results are well presented are are relevant to the field of representation learning.

Cons:
 - The figures are not grayscale friendly, which made it harder to understand some important points. Once I saw the color figures, it made things much clearer.

Small nit:
In section 3.2, the authors use the term 'significantly' when there are no uncertainties on the performance measure. Perhaps using another term would be more appropriate.