Relations between Coins and other datasets


Classification of images has 2 distinct fields – differing in the quantity of classes and categories involved. Most studied are the multi-category datasets where subsets of images drastically differ in appearance from the others.

Sample images from the Caltech-101 dataset. Link

These datasets while difficult, are relatively easy when compared to the multi-class datasets. This is because images belonging to a particular category can be visually different from the other images. For instance a soccer ball is very different from an airplane when color histograms are compared.

Birds and flowers have recently become popular datasets because while the images can still be very different, they are more similar than prior datasets. For this reason they belong in the multi-class type of classification.

Flowers dataset. Link

Caltech-UCSD Birds 200. Link

Coins dataset. Link

As it can be seen, coins is perhaps an extreme example of the multi-class image classification problem. While a numismatic expert may argue that coins from different regions of the world could be split by category, and denominations within the region are actually classes, for the untrained eye (ie. computers) they are pretty much all the same. Unlike flowers where color and petal shape can aid in the classification process coins have neither. Also coins have no “poses” as is the case with birds which has been shown to be an important feature. These omissions have all been used in the recent past in creating state-of-the-art classification algorithms for the Bird and Flower datasets. While birds and flowers have a relatively small number of visual characteristics coins have complex scenes that have been painstakingly catalogued by humans.