Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
| Autor: | Mohammad Rahmati, Maryam Abdolali |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Information Systems and Management Optimization problem Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Theoretical Computer Science Matrix (mathematics) Subspace clustering Discriminative model Artificial Intelligence Robustness (computer science) Grassmannian 0202 electrical engineering electronic engineering information engineering business.industry 05 social sciences 050301 education Pattern recognition Computer Science Applications Data set Control and Systems Engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business 0503 education Software |
| DOI: | 10.48550/arxiv.1905.07220 |
| Popis: | In this study, we consider the problem of subspace clustering in the presence of spatially contiguous noise, occlusion, and disguise. We argue that self-expressive representation of data, which is a key characteristic of current state-of-the-art approaches, is severely sensitive to occlusions and complex real-world noises. To alleviate this problem, we highlight the importance of previously neglected local representations in improving robustness and propose a hierarchical framework that combines the robustness of local-patch-based representations and the discriminative property of global representations. This approach consists of two main steps: 1) A top-down stage, in which the input data are subject to repeated division to smaller patches and 2) a bottom-up stage, in which the low rank embedding of representation matrices of local patches in the field of view of a corresponding patch in the upper level are merged on a Grassmann manifold. This unified approach provides two key pieces of information for neighborhood graph of the corresponding patch on the upper level: cannot-links and recommended-links. This supplies a robust summary of local representations which is further employed for computing self-expressive representations using a novel weighted sparse group lasso optimization problem. Numerical results for several data sets confirm the efficiency of our approach. |
| Databáze: | OpenAIRE |
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