Paper Info
Title
Locality-Aware Group Sparse Coding on Grassmann Manifolds for Image Set Classification
Abstract
Riemannian sparse coding methods are attracting increasing interest in many computer vision applications, relying on its non-Euclidean structure. One such recently successful task is image set classification by the aid of Grassmann Manifolds, where an image set can be seen as a point. However, due to irrelevant information and outliers, the probe set may be represented by misleading sets with large sparse coefficients. Meanwhile, it is difficult for a single subspace to cover changes within an image set and the hidden structure among samples is relaxed. In this paper, we propose a novel Grassmann Locality-Aware Group Sparse Coding model (GLGSC) that attempts to preserve locality information and take advantage of the relationship among image sets to capture the inter and intra-set variations simultaneously. Since the contributions of different gallery subspaces to the probe subspace should vary in importance, we then introduce a novel representation adaption term. In addition, a kernelised version of GLGSC is proposed to handle non-linearity in data. To reveal the effectiveness of our algorithm over state-of-the-art, several classification tasks are conducted, including face recognition, object recognition and gesture recognition.
Year
DOI
Venue
2020
10.1016/j.neucom.2019.12.026
Neurocomputing
Keywords
Field
DocType
Grassmann manifolds,Group sparse coding,Locality preserving,Image set classification
Facial recognition system,Locality,Subspace topology,Pattern recognition,Neural coding,Gesture recognition,Linear subspace,Artificial intelligence,Manifold,Mathematics,Cognitive neuroscience of visual object recognition
Journal
Volume
ISSN
Citations 
385
0925-2312
1
PageRank 
References 
Authors
0.35
39
5
Name
Order
Citations
PageRank
dong wei1166.83
Xiao-Bo Shen220921.35
Quansen Sun3122283.09
Xizhan Gao4287.17
Wenzhu Yan562.84