| Full text | |
| Author(s): |
Valem, Lucas Pascotti
[1]
;
De Oliveira, Carlos Renan
[1]
;
Guimaraes Pedronette, Daniel Carlos
[1]
;
Almeida, Jurandy
[2]
Total Authors: 4
|
| Affiliation: | [1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp, Av 24-A, 1515, BR-13506900 Rio Claro, SP - Brazil
[2] Univ Fed Sao Paulo UNIFESP, Inst Ciencia & Tecnol, Av Cesare MG Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | ACM Transactions on Multimedia Computing Communications and Applications; v. 14, n. 4 NOV 2018. |
| Web of Science Citations: | 1 |
| Abstract | |
The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets. (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: | 17/02091-4 - Selection and combination of unsupervised learning Methdos for content-based image retrieval |
| Grantee: | Lucas Pascotti Valem |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 16/06441-7 - Semantic information retrieval in large video databases |
| Grantee: | Jurandy Gomes de Almeida Junior |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants |