Instead of thinking of compression as something that is used to reduce the size of data, consider it as a measure of understanding data that generalizes well to unseen data. One should view compression as understanding.
Imagine that you walk to class using your normal route while listening to your ipod. Even though you were aware of your surroundings while walking, it was most likely an indirect experience where you remember only subtle little details related to the walk. However, you can still be 100% sure that you had taken the same path as last time. While you were walking, your brain 'understood' the environment dynamically and it only needed a low bit stream (dynamically compressed) of visual information to localize you. This of this notion of compression as model fitting, where the objects of perception are the model parameters and the raw data is inaccesible.
Even though your experience of walking lasted 20 minutes, you feel like you didn't acquire much experience as opposed to spending 20 minutes in a completely new place. Your brain selectively took in information; you might have remembered seeing somebody you recognize drive by but forgot some of the songs that you listened to.
The notion of "an object recognition system" using a "segmentation algorithm" is the traditional definition of segmentation as a mid-level process and object recognition as a high-level process. However, you can't really segment until you've recognized. Recognition and segmentation should be viewed on an equal footing; unified.