Abstract:
In the past few years, many object classification techniques have been developed.
However, these techniques do not perform very well when categorizing
objects belonging to fine-grained categories. It is easier to differentiate between
objects belonging to different broader categories as they have different higherlevel
features or parts, like differentiating a dog from a car is an easy task. But
when it comes to distinguishing between objects belonging to same category, it
can be a challenging task as there is very little variance among their parts, like
distinguishing between dogs belonging to different breeds. The task is not only
to find parts of an object, but to find discriminative parts that help us categorize
objects correctly. Recently part-based techniques have shown promising results
in fine-grained categorization.
