| Full text | |
| Author(s): |
Peixinho, Alan Z.
;
Benato, Barbara C.
;
Nonato, Luis G.
;
Falcao, Alexandre X.
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2018-01-01. |
| Abstract | |
It is well known that image classification problems can be effectively solved by Convolutional Neural Networks (CNNs). However, the number of supervised training examples from all categories must be high enough to avoid model over-fitting. In this case, two key alternatives are usually presented (a) the generation of artificial examples, known as data augmentation, and (b) reusing a CNN previously trained over a large supervised training set from another image classification problem - a strategy known as transfer learning. Deep learning approaches have rarely exploited the superior ability of humans for cognitive tasks during the machine learning loop. We advocate that the expert intervention through visual analytics can improve machine learning. In this work, we demonstrate this claim by proposing a data augmentation framework based on Encoder-Decoder Neural Networks (EDNNs) and visual analytics for the design of more effective CNN-based image classifiers. An EDNN is initially trained such that its encoder extracts a feature vector from each training image. These samples are projected from the encoder feature space on to a 2D coordinate space. The expert includes points to the projection space and the feature vectors of the new samples are obtained on the original feature space by interpolation. The decoder generates artificial images from the feature vectors of the new samples and the augmented training set is used to improve the CNN-based classifier. We evaluate methods for the proposed framework and demonstrate its advantages using data from a real problem as case study - the diagnosis of helminth eggs in humans. We also show that transfer learning and data augmentation by affine transformations can further improve the results. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/25776-0 - Autoencoders neural networks optimization by visual analytics data |
| Grantee: | Bárbara Caroline Benato |
| Support Opportunities: | Scholarships in Brazil - Master |