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]:
Very interesting paper on different kinds of feature representations for agglomeration of 3D neuron fragments. The paper demonstrates how the performance using a set of hand designed features can be further improved with representations derived using both end-to-end and unsupervised training techniques on this task. Several interesting machine learning techniques for feature discovery and training, adopted in other fields have been successfully deployed for this task.

- Well written paper with solid results evaluating different novel features and their combinations, for agglomeration of 3D neuron fragments.
 - Several powerful techniques - end-to-end learning/unsupervised training/dynamic pooling, have been successfully used for this task.

The paper is quite domain specific although novel in its domain. It does not seem to prescribe any new take-home techniques for other tasks/domains.

The features and machine learning techniques introduced in the paper produce remarkably high performance results. It would be useful to have more insights on the nature of the task and how challenging it is in general.  The proposed 'boundary map features' are capable of achieving 90% AUC for the task (Table 1) - Why is that? - Is this something specific to the task? It would be useful  to understand how other features (object/image based features) perform by themselves (Table 1) - the results presented for these features are after combination only. Do they perform only in combination or do they also have a base performance of 90% by themselves?

The paper shows very clear benefits of using features from unsupervised learning techniques. What kind of attributes are these features learning? Are they similar to the boundary map features and hence the high performance as well? More insights would be useful.