  In this paper, we address the problem of action recognition from still images
and videos. Traditional local features such as SIFT, STIP etc. invariably pose
two potential problems: 1) they are not evenly distributed in different
entities of a given category and 2) many of such features are not exclusive of
the visual concept the entities represent. In order to generate a dictionary
taking the aforementioned issues into account, we propose a novel
discriminative method for identifying robust and category specific local
features which maximize the class separability to a greater extent.
Specifically, we pose the selection of potent local descriptors as filtering
based feature selection problem which ranks the local features per category
based on a novel measure of distinctiveness. The underlying visual entities are
subsequently represented based on the learned dictionary and this stage is
followed by action classification using the random forest model followed by
label propagation refinement. The framework is validated on the action
recognition datasets based on still images (Stanford-40) as well as videos
(UCF-50) and exhibits superior performances than the representative methods
from the literature.
