| Full text | |
| Author(s): |
Camacho Presotto, Joao Gabriel
;
Valem, Lucas Pascotti
;
Pedronette, Daniel Carlos Guimaraes
;
Vento, M
;
Percannella, G
Total Authors: 5
|
| Document type: | Journal article |
| Source: | COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II; v. 11679, p. 14-pg., 2019-01-01. |
| Abstract | |
Estimating the effectiveness of retrieval systems in unsupervised scenarios consists in a task of crucial relevance. By exploiting estimations which dot not require supervision, the retrieval results of many applications as rank aggregation and relevance feedback can be improved. In this paper, a novel approach for unsupervised effectiveness estimation is proposed based the intersection of ranking references at top-k positions of ranked lists. An experimental evaluationwas conducted considering public datasets and different image features. The linear correlation between the proposed measure and the effectiveness evaluation measures was assessed, achieving high scores. In addition, the proposed measure was also evaluated jointly with rank aggregationmethods, by assigning weights to ranked lists according to the effectiveness estimation of each feature. (AU) | |
| 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 |
| 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 |