| Full text | |
| Author(s): |
Pereira-Ferrero, Vanessa Helena
;
Valem, Lucas Pascotti
;
Leticio, Gustavo Rosseto
;
Pedronette, Daniel Carlos Guimaraes
Total Authors: 4
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING; v. 18, n. 04, p. 22-pg., 2024-08-16. |
| Abstract | |
Despite the great advances in the field of image classification, the association of ideal approaches that can bring improved results, considering different datasets, is still an open challenge. In this work, a novel approach is presented, based on a combination of compared strategies: feature extraction for early fusion; rankings based on manifold learning for late fusion; and feature augmentation applied in a long short-term memory (LSTM) algorithm. The proposed method aims to investigate the effect of feature fusion (early fusion) and ranking fusion (late fusion) in the final results of image classification. The experimental results showed that the proposed strategies improved the accuracy of results in different tested datasets (such as CIFAR10, Stanford Dogs, Linnaeus 5, Flowers 102, and Flowers 17) using a fusion of features from three convolutional neural networks (CNNs) (ResNet152, VGG16, and DPN92) and its respective generated rankings. The results indicated significant improvements and showed the potential of the approach proposed for image classification. (AU) | |
| 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: | 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 |