Learning spatial relations and shapes for structural object description and scene recognition

Being able to describe the content of an image, adapted to a particular application, is essential in various domains related to image analysis and pattern recognition. In this context, taking into account the spatial organization of objects is fundamental to increase both the understanding and the accuracy of the perceived similarity between images. In this article, we first present the Force Histogram Decomposition (FHD), a graph-based hierarchical descriptor that allows to characterize the spatial relations and shape information between the pairwise structural subparts of objects. Then, we propose a novel bags-of-features framework based on such descriptors, in order to produce discriminative structural features that are tailored for particular object classification tasks. An advantage of this learning procedure is its compatibility with traditional bags-of-features frameworks, allowing for hybrid representations gathering structural and local features. Experimental results obtained both on the recognition of structured objects from color images and on a parts-based scene recognition task highlight the interest of this approach.