| Full text | |
| Author(s): |
Pereira-Ferrero, Vanessa Helena
;
Valem, Lucas Pascotti
;
Pedronette, Daniel Carlos Guimaraes
Total Authors: 3
|
| Document type: | Journal article |
| Source: | EXPERT SYSTEMS WITH APPLICATIONS; v. 213, p. 16-pg., 2023-03-01. |
| Abstract | |
Image classification is a critical topic due to its wide application and several challenges associated. Despite the huge progress made last decades, there is still a demand for context-aware image representation approaches capable of taking into the dataset manifold for improving classification accuracy. In this work, a representation learning approach is proposed, based on a novel feature augmentation strategy. The proposed method aims to exploit available contextual similarity information through rank-based manifold learning used to define and assign weights to samples used in augmentation. The approach is validated using CNN-based features and LSTM models to achieve even higher accuracy results on image classification tasks. Experimental results show that the feature augmentation strategy can indeed improve the accuracy of results on widely used image datasets (CIFAR10, Stanford Dogs, Linnaeus5, Flowers102 and Flowers17) in different CNNs (ResNet152, VGG16, DPN92). The results indicate gains up to 20% and show the potential of the developed approach in achieving higher accuracy results for image classification. (AU) | |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 20/02183-9 - Rank-based unsupervised learning through deep learning in diverse domains |
| Grantee: | Vanessa Helena Pereira Ferrero |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 20/11366-0 - Support for computational environments and experiments execution: weakly-supervised and classification fusion methods |
| Grantee: | Lucas Pascotti Valem |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |