Tensor scale is a morphometric parameter that unifies the representation of local structure thickness, orientation, and anisotropy, which can be used in several computer vision and image processing tasks. We exploit this concept for binary images and propose two shape descriptors – Tensor Scale Descriptor with Influence Zones and Tensor Scale Contour Saliences. It also introduces a robust method to compute tensor scale, using a graph-based approach – the image foresting transform. Experimental results are provided, showing the effectiveness of the proposed methods, when compared to other relevant methods with regard to their use in content-based image retrieval tasks.