Sparse Representation, Modeling and Learning in Visual Recognition (Theory, Algorithms and Applications)
Over the past decade, sparse representation, modeling, and learning has emerged and is widely used in many visual tasks such as feature extraction and learning, object detection, and recognition (i.e., faces, activities). It is rooted in statistics, physics, information theory, neuroscience, opti...
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| Tác giả chính: | |
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
Springer
2015
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57200 |
| Các nhãn: |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Tóm tắt: | Over the past decade, sparse representation, modeling, and learning has emerged
and is widely used in many visual tasks such as feature extraction and learning,
object detection, and recognition (i.e., faces, activities). It is rooted in statistics,
physics, information theory, neuroscience, optimization theory, algorithms, and
data structure. Meanwhile, visual recognition has played a critical role in computer
vision as well as in robotics. Recently, sparse representation consists of two basic
tasks, data sparsification and encoding features. The first task is to make data more
sparse directly. The second is to encode features with sparsity properties in some
domain using either strictly or approximately K-Sparsity. Sparse modeling is to
model specific tasks by jointly using different disciplines and their sparsity prop
erties. Sparse learning is to learn mapping from input signals to outputs by either
representing the sparsity of signals or modeling the sparsity constraints as regu
larization items in optimization equation. Mathematically, solving sparse repre
sentation and learning involves seeking the sparsest linear combination of basic
functions from an overcomplete dictionary. The rationale behind this is the sparse
connectivity between nodes in the human brain... |
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