Persistence-Based Structural Recognition

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 2003 - 2010
• Main Authors: Chunyuan Li, Ovsjanikov, Maks, Chazal, Frederic
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Computer vision; features; Invariants; Kernel; Mathematical analysis; Mathematical models; Measurement; Object recognition; Pattern recognition; persistent homology; Recognition; Robustness; Shape; Stability analysis; Surface layer; Texture; Three dimensional; Three-dimensional displays; Topological Data Analysis; Vectors
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2014.257

This paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, which captures the distribution 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, including 2D shapes, textures and triangle meshes. Extensive experiments on 3D shape retrieval, hand gesture recognition and texture classification demonstrate the performance of the proposed method in comparison with state-of-the-art methods. Additionally, our approach yields higher recognition accuracy when used in conjunction with the bag-of-features.