Towards automated conceptual shape-based characterization an application to symbolic image retrieval

We propose a framework highlighting symbolic shape concepts based on the characterization of their geometrical properties. Starting from seven basic shapes, we define transformations to generate novel shapes and discuss their organization within a lattice-based structure. These are then automatically assigned a conceptual representation after: (i) the extraction of low-level shape features based on Fourier descriptors, (ii) the mapping of the low-level features with shape concepts through a support vector matching architecture featuring a radial basis function kernel. Experimentally, we compute the accuracy of the symbolic shape characterization through 5-fold cross validation and demonstrate the effectiveness of the shape concepts for symbolic image retrieval. We indeed show, in a recall-precision evaluation framework, that our approach outperforms a state-of-the-art content-based image retrieval architecture based on query-by-example.