A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Proceedings of the IFIP TC2/WG2.6 Sixth Working Conference on Visual Database Systems: Visual and Multimedia Information Management
Aspect-based relevance learning for image retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Performance evaluation of relevance feedback methods
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
| Hi-index | 0.00 |
Aspect-based relevance learning is a relevance feedback scheme based on a natural model of relevance in terms of image aspects. In this paper we propose a number of active learning and interaction strategies, capitalizing on the transparency of the aspect-based framework. Additionally, we demonstrate that, relative to other schemes, aspect-based relevance learning upholds its retrieval performance well under feedback consisting mainly of example images that are only partially relevant.