  Action recognition from still images is an important task of computer vision
applications such as image annotation, robotic navigation, video surveillance
and several others. Existing approaches mainly rely on either bag-of-feature
representations or articulated body-part models. However, the relationship
between the action and the image segments is still substantially unexplored.
For this reason, in this paper we propose to approach action recognition by
leveraging an intermediate layer of "superpixels" whose latent classes can act
as attributes of the action. In the proposed approach, the action class is
predicted by a structural model(learnt by Latent Structural SVM) based on
measurements from the image superpixels and their latent classes. Experimental
results over the challenging Stanford 40 Actions dataset report a significant
average accuracy of 74.06% for the positive class and 88.50% for the negative
class, giving evidence to the performance of the proposed approach.
