Persistence-based Structural Recognition

Published

June 28, 2014

Author(s)

Chunyuan Li

Abstract

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.

Conference Dates

June 24-27, 2014

Conference Location

Columbus, OH

Conference Title

IEEE Conference on Computer Vision and Pattern Recognition 2014

Pub Type

Conferences

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DOI Link

Keywords

topological persistence, shape retrieval, hand gesture recognition, texture classification

Citation

Li, C. (2014), Persistence-based Structural Recognition, IEEE Conference on Computer Vision and Pattern Recognition 2014, Columbus, OH, [online], https://doi.org/10.1109/CVPR.2014.257 (Accessed October 7, 2024)