Sentence examples for collaborative retrieval from inspiring English sources

Engaging in collaborative retrieval practice increases performance on subsequent memory tests taken individually (Blumen & Stern, 2011).

The relevance feedback considers a single query interaction, while the collaborative retrieval scenario exploits multiple interactions available in different queries submitted to the search system.

Fig. 13 Collaborative image retrieval: evolution of P @20 for each iteration considering texture descriptors Fig. 14 Collaborative image retrieval: comparison of precision × recall for texture descriptors before and after 10 iterations.

Fig. 5 Collaborative image retrieval: evolution of P @20 measure for each iteration considering shape descriptors Fig. 6 Collaborative image retrieval: comparison of precision × recall for shape descriptors before and after 10 iterations.

Fig. 9 Collaborative image retrieval: evolution of P @20 measure for each iteration considering color descriptors Fig. 10 Collaborative image retrieval: comparison of precision × recall for color descriptors before and after 10 iterations.

The collaborative image retrieval scenario aims at modeling a real-world situation in which various users submit simultaneous queries to a retrieval system and provide their relevance feedback.

In "A Semi-Supervised LeaRelevanceorithm Feedbackvande FeedbaCollaborativeoratImagemage Retrieval," Pedronette et al. highlight the power of this approach for communities of users participating in collaborative image retrieval.

The collaborative image retrieval approach was evaluated using the same experimental setup of the relevance feedback.

In collaborative image retrieval tasks, the semi-supervised learning algorithm benefits from feedbacks of different users.

Fig. 2 Collaborative image retrieval workflow based on the semi-supervised proposed framework.

Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios.