Persistence-based Structural Recognition

This paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions de-fined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, which captures the distribu-tion of values of a given function, persistence diagrams can be used to characterize its structural properties, reflecting spatial information in an invariant way. In practice, the choice of function is simple: each dimension of the feature vector can be viewed as a function. The proposed method is general: it can work on various multimedia data, includ-ing 2D shapes, textures and triangle meshes. Extensive experiments on 3D shape retrieval, hand gesture recogni-tion and texture classification demonstrate the performance of the proposed method in comparison with state-of-the-art methods. Additionally, our approach yields higher recog-nition accuracy when used in conjunction with the bag-of-features. 1