In the previous post:

Recall = Number of retrieved images that are also relevant / Total number of relevant images.

Precision = Number of retrieved images that are also relevant / Total number of retrieved images.

In Pixcavator Search, the matches are simply ordered based on their distance from the query (just like Google). Then we need choose a cut-off. In the example considered last time, the cut-off was implicitly “all that fit in one page”. This is a reasonable standard for user oriented applications. For experimentation and testing, however, we may want to use the distance instead. For example, below I may choose the cut-off distance of 80: all images within 80 from the query are declared matches and retrieved, the rest are not. Then recall = 7/8, precision = 7/14.

The choice was made based on the examination of the search results for this particular image in an attempt to include as many as possible of “good” matches and, at the same time, to exclude as many as possible of the “bad” matches. More experimentation showed that 80 works OK for other queries as well. In general, however, this is not to be expected.

My conclusion is that the main drawback of precision and recall as a measure quality of the algorithm is that it requires a cut-off to separate the retrieved images from the rest. Then, the evaluation results depend on this choice. In fact, this measure ends up to be a measure of the quality of the query image, not the algorithm.