| Full text | |
| Author(s): |
Kawai, Vinicius Sato
;
Valem, Lucas Pascotti
;
Baldassin, Alexandro
;
Borin, Edson
;
Pedronette, Daniel Carlos Guimaraes
;
Latecki, Longin Jan
Total Authors: 6
|
| Document type: | Journal article |
| Source: | ACM Transactions on Multimedia Computing Communications and Applications; v. 20, n. 10, p. 19-pg., 2024-10-01. |
| Abstract | |
The large and growing amount of digital data creates a pressing need for approaches capable of indexing and retrieving multimedia content. A traditional and fundamental challenge consists of effectively and efficiently performing nearest-neighbor searches. After decades of research, several different methods are available, including trees, hashing, and graph-based approaches. Most of the current methods exploit learning to hash approaches based on deep learning. In spite of effective results and compact codes obtained, such methods often require a significant amount of labeled data for training. Unsupervised approaches also rely on expensive training procedures usually based on a huge amount of data. In this work, we propose an unsupervised data- independent approach for nearest neighbor searches, which can be used with different features, including deep features trained by transfer learning. The method uses a rank-based formulation and exploits a hashing approach for efficient ranked list computation at query time. A comprehensive experimental evaluation was conducted on seven public datasets, considering deep features based on CNNs and Transformers. Both effectiveness and efficiency aspects were evaluated. The proposed approach achieves remarkable results in comparison to traditional and state-of-the-art methods. Hence, it is an attractive and innovative solution, especially when costly training procedures need to be avoided. (AU) | |
| FAPESP's process: | 18/15519-5 - Performance optimizations for multicore architectures |
| Grantee: | Alexandro José Baldassin |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 23/00694-4 - Consistent and explainable natural language inference |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Regular Research Grants |
| 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 |