| Full text | |
| Author(s): |
Calumby, Rodrigo Tripodi
;
Goncalves, Marcos Andre
;
Torres, Ricardo da Silva
Total Authors: 3
|
| Document type: | Journal article |
| Source: | Neurocomputing; v. 259, p. 17-pg., 2017-10-11. |
| Abstract | |
In interactive retrieval tasks, one of the main objectives is to maximize the user information gain throughout search sessions. Retrieving many relevant items is quite important, but it does not necessarily completely satisfy the user needs. When only relevant near-duplicate items are retrieved, the amount of different concepts users are able to extract from the target collection is very limited. Therefore, broadening the number of concepts present in a result set may improve the overall search experience. Diversifying concepts present in the retrieved set is one possibility for increasing the information gain in a single search iteration, maximizing the likelihood of including at least some relevant items for each possible intent of ambiguous or underspecified queries. Relevance feedback approaches may also take advantage of diverse results to improve internal machine learning models. In this context, this work proposes and analyses several multimodal image retrieval approaches built over a learning framework for relevance feedback on diversified results. Our experimental analysis shows that different retrieval modalities are positively impacted by diversity, but achieve best retrieval effectiveness with diversification applied at different moments of a search session. Moreover, the best results are achieved with a query-by-example approach using multimodal information obtained from feedback. In summary, we demonstrate that learning with diversity is an effective alternative for boosting multimodal interactive learning approaches. (C) 2017 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes |
| Grantee: | Ricardo da Silva Torres |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems |
| Grantee: | Leonor Patricia Cerdeira Morellato |
| Support Opportunities: | Research Program on Global Climate Change - University-Industry Cooperative Research (PITE) |