I was also a bit disappointed by the the lack of clear goals for the image retrieval system, and results that make it possible to compare it with other approaches.

The only quantitative performance results that the authors show are precision and recall curves. Although this gives an idea of how well the algorithm ranks the images as belonging to a certain class, the authors don't give any information into how many images users are likely to scan in order to find what they are looking for. In other words, they don't say what is the operation point on these curves for real-world applications. Also, they say the curves show the average precision and recall for each class but they don't put error bars indicating what is the standard deviation on the precision and recall within each class. The error bars would show how their algorithm varies depending on the initial positive training set selected by the user.

Like Sameer, I am also confused by how the learning proceeds after the user labels additional examples.