Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Tracking scalar features in unstructured datasets
Proceedings of the conference on Visualization '98
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning to Decode Cognitive States from Brain Images
Machine Learning
Topographic Independent Component Analysis
Neural Computation
On image retrieval using salient regions with vector-spaces and latent semantics
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Effectiveness of the finite impulse response model in content-based fMRI image retrieval
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
| Hi-index | 0.00 |
In this paper, we explore the concept of a "library of brain images", which implies not only a repository of brain images, but also efficient search and retrieval mechanisms that are based on models derived from IR practice. As a preliminary study, we have worked with a collection of functional MRI brain images assembled in the study of several distinct cognitive tasks. We adapt several classical IR methods (inverted indexing, TFIDF and Latent Semantic Indexing(LSI)) to content-based brain image retrieval. Our results show that efficient and accurate retrieval of brain images is possible, and that representations motivated by the IR perspective are somewhat more effective than are methods based on retaining the full image information.