Graphical Gaussian Vector for Image Categorization

This paper proposes a novel image representation called a Graphical Gaussian Vector, which is a counterpart of the codebook and local feature matching approaches. In our method, we model the distribution of local features as a Gaussian Markov Random Field (GMRF) which can efficiently represent the spatial relationship among local features. We consider the parameter of GMRF as a feature vector of the image. Using concepts of information geometry, proper parameters and a metric from the GMRF can be obtained. Finally we define a new image feature by embedding the metric into the parameters, which can be directly applied to scalable linear classifiers.