Barycentric Coordinates Based Soft Assignment for Object Classification

For object classification, soft assignment (SA) is capable
of improving the bag-of-visual-words (BoVW) model and
has the advantages in conceptual simplicity. However, the
performance of soft assignment is inferior to those recently
developed encoding schemes. In this paper, we propose a
novel scheme called barycentric coordinates based soft assignment
(BCSA) for the classification of object images.
While maintaining conceptual simplicity, this scheme will be
shown to outperform most of the existing encoding schemes,
including sparse and local coding schemes. Furthermore, with
only single-scale features, it is able to achieve comparable or
even better performance to current state-of-the-art Fisher kernel
(FK) encoding scheme. In particular, the proposed BCSA
scheme enjoys the following properties: 1) preservation of
linear order precision for encoding which makes BCSA robust
to linear transform distortions; 2) inheriting naturally
the visual word uncertainty which leads to a more expressive
model; 3) generating linear classifiable codes that can be
learned with significant less computational cost and storage.
Extensive experiments based on widely used Caltech-101 and
Caltech-256 datasets have been carried out to show its effectiveness
of the proposed BCSA scheme in both performance
and simplicity.