  Superpixel algorithms aim to over-segment the image by grouping pixels that
belong to the same object. Many state-of-the-art superpixel algorithms rely on
minimizing objective functions to enforce color ho- mogeneity. The optimization
is accomplished by sophis- ticated methods that progressively build the
superpix- els, typically by adding cuts or growing superpixels. As a result,
they are computationally too expensive for real-time applications. We introduce
a new approach based on a simple hill-climbing optimization. Starting from an
initial superpixel partitioning, it continuously refines the superpixels by
modifying the boundaries. We define a robust and fast to evaluate energy
function, based on enforcing color similarity between the bound- aries and the
superpixel color histogram. In a series of experiments, we show that we achieve
an excellent com- promise between accuracy and efficiency. We are able to
achieve a performance comparable to the state-of- the-art, but in real-time on
a single Intel i7 CPU at 2.8GHz.
