We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, ...
Many of the available image databases have keyword annotations associated with the images. In spite of the availability of good quality low-level visual features that reflect wel...
In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). However, since users are usually unwillin...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Wei-Ying Ma, ...
Since multimedia information is characterized by motley types of media with different properties, multimedia content retrieval in digital libraries requires dynamic reconfigurable...
Panagiotis Karagiannis, Nikolaos D. Doulamis, Geor...
Small-sample learning in image retrieval is a pertinent and interesting problem. Relevance feedback is an active area of research that seeks to find algorithms that are robust wi...
Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. H...
Search systems have for some time provided users with the ability to request documents similar to a given document. Interfaces provide this feature via a link or button for each d...
Information retrieval algorithms leverage various collection statistics to improve performance. Because these statistics are often computed on a relatively small evaluation corpus...
We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into...
Relevance feedback is the state-of-the-art approach for adjusting query results to the needs of the users. This work extends the existing framework of image retrieval with relevan...
Euripides G. M. Petrakis, Klaydios Kontis, Epimeni...
Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalabil...