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.