Bio-medical Ontologies Maintenance and Change Management

Multimedia Medical Databases 101
Then:
Recall = a / (a + c)
(3.22)
Precision = a / (a + b)
In practice, there are considered both parameters: recall and precision. In this
case when the recall is increasing, the precision is decreasing. This is happening
because when the system tries to find all relevant articles for a query, it also finds
non-relevant articles. The result is a decrease of the precision.
A system with a high value for recall, but with a low value for precision, will
return a long list of retrieved articles, but a lot of them are irrelevant. On the other
hand, a system with a high value for precision and a small value for the recall parameter
indicates that there are many relevant records that haven’t been retrieved.
In conclusion, a good retrieval system must have a balance between those two
parameters. A modality to do that is to determine the values of precision and recall
(values between 0 and 1) and to built a drawing precision/recall for each system in
part (as in figure 3.7). The system that has the drawing to a bigger distance from
origin has a better performance.
Precision
1
A
B
Fig. 3.7. The Recall/Precision drawing. A system with the drawing to a higher distance from
origin has a better performance. The system with the drawing B is better than the system with
drawing A.
3.7 Content-Based Visual Retrieval on Color Features – Experiments and
Results
For the content-based image retrieval systems that use in practice medical multimedia
databases have been considered especially grey level features in local or
global fashion and less color features.
For example, in the IRMA project (Image Retrieval in Medical Applications)
the image data consists of radiographs, while in the later phases will deal with
1
Recall