| Full text | |
| Author(s): |
de Souza, Italos Estilon
;
Benato, Barbara C.
;
Falcao, Alexandre Xavier
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020); v. N/A, p. 8-pg., 2020-01-01. |
| Abstract | |
Convolutional neural networks (CNNs) have been used in several computer vision applications. However, most well-succeeded models are usually pre-trained on large labeled datasets. The adaptation of such models to new applications (or datasets) with no label information might be an issue, calling for the construction of a suitable model from scratch. In this paper, we introduce an interactive method to estimate CNN filters from image markers with no need for backpropagation and pre-trained models. The method, named FLIM (feature learning from image markers), exploits the user knowledge about image regions that discriminate objects for marker selection. For a given CNN's architecture and user-drawn markers in an input image, FLIM can estimate the CNN filters by clustering marker pixels in a layer-by-layer fashion - i.e., the filters of a current layer are estimated from the output of the previous one. We demonstrate the advantages of FLIM for object delineation over alternatives based on a state-of-the-art pre-trained model and the Lab color space. The results indicate the potential of the method towards the construction of explainable CNN models. (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: | 19/10705-8 - Visual Active Learning guided by Feature Projections |
| Grantee: | Bárbara Caroline Benato |
| Support Opportunities: | Scholarships in Brazil - Doctorate |