Report on the Annotation Task in ImageCLEFmed 2005

The ImageCLEF 2012 Scalable Image Annotation Using Gen- eral Web Data Task proposed a challenge, in which as training data instead of relying only on a set of manually annotated

The findings according to the official evaluations confirm that: late fusion of multiple features lead to good result, that dimension reduction on few features is an

The ImageCLEF 2010 Photo Annotation Task posed a multi-label annotation challenge for visual concept detection in three general configurations (textual, visual and multi-modal)..

Our annotation method takes into account image features as well as their context links (taken from tags in the MIR Flickr collection) in order to achieve SVM learning

Automatic Image Annotation, Scale Invariant Feature Transform, Edge Histogram Descriptor, Hierarchical Multi-Label Classification, Predictive Clustering Trees, Random Forests.. 1

They extract global color and texture as features; calculate image similarity as the average distance using these features; and the keywords are obtained from the nearest neighbors

The number of visual runs in 2008 was much lower than in previous years, and the evolution is not as fast as with textual retrieval techniques.. Five groups submitted a total of

In Figure 2, some example test images are given along with their full IRMA code, the number of wildcards used by the submitted runs on average and the number of training images