Medical Image Classification via 2D color feature based Covariance Descriptors

In these notes we present an image classification method which has been submitted to the ImageCLEF 2015 Medical Classification challenge. The aim is to classify images from 30 heterogeneous classes ranging from diagnose images coming from different acquisition techniques, to various biomedical publication illustrations. The presented work is intended to be a proof of concept of how our method, which uses only visual information, performs in the modelling of such image classes. Our approach uses 1 and 2 order color features obtained at a whole image level. These features are considered as samples of a multidimensional statistical distribution, and a distinctive signature of the represented image can be built in the form of a Covariance-matrix based descriptor. The Riemannian manifold structure of such descriptors can be exploited in order to formulate an image classification methodology. Despite the challenging task due to unbalanced classes and image homogeneity, the obtained results in the task place our method on the top of the most accurate ones using purely visual features. This asserts the feasibility of our methodology and proves that its performance can be on par with other methods which use also complementary textual features for complex image retrieval.