Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.

Teacher-learning process based on an immersive and interactive environment

This paper explores the benefits of using immersive and interactive multiprojection environments (CAVE) to visualize molecules, and how it improves users’ understanding. We have proposed and implemented a tool for teachers to manipulate molecules and another to edit molecules and assist students at home. The contribution of the present research project are these tool that allows investigating structures, properties and dynamics of a molecular system which are extremely complex and comprises millions of atoms. The experience is enriched through multimedia information associated with parts of the model; for example, videos and text can be linked to specific molecule, demonstrating some detail. This solution is based on a teaching-learning process.