该方法的核心是选择一个明智的字典作为代表，用来测试信号稀疏线性组合信号。我们首先要简单的了解令人诧异的人脸识别途径是有效的解决办法。反过来，人脸识别实例在稀疏表示光曝光之前揭示了新的理论现象。
之前稀疏表示的部分用机器检查并且应用，在一个完全词典里组成的语义信息本身产生的样品。对于许多数据不是简单的应用，这是合乎情理的词典，使用一个紧凑的数据得到优化目标函数的一些任务。本节概述学习方法那种词典，以及这些方法应用在计算机视觉和图像处理。
通过近年来我们对稀疏编码和优化的应用的理解和启发，如面部识别一节描述的例子，我们提出通过稀疏数据编码构造，利用它建立了受欢迎的机器学习任务。在一个图的数据推导出研究学报。2009年3月5乘编码每个数据稀疏表示的剩余的样本，并自动选择最为有效的邻居为每个数据。通过minimization稀疏表示的计算自然的性能满足净水剂结构。 此外，我们将会看到描述之间的关系进行了实证minimization线性数据的性能，可以显著提高现有的基于图论学习算法可行性。
摘自：期刊IEEE的论文- PIEEE ，第一卷
英文翻译
eyeqSPARSE REPRESENTATION FOR COMPUTER VISION AND PATTERN RECOGNITIO
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Abstract—Techniques from spar signal reprentation are beginning to e significant impact in computer vision, often on non-traditional applications where the goal is not just to obtain a compact high-fidelity reprentation of the obrved signal, but also to extract mantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themlves provide the key to obtaining state-of-theart results and to attaching mantic meaning to spar signal reprentations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few reprentative examples of how the interaction between spar signal reprentation and computer vision can enrich both fields and rais a number of open questions for further study.
Spar signal reprentation has proven to be an extremely powerful tool for acquiring, reprenting, and compressing high-dimensional signals. This success is mainly due to tvagina是什么意思
he fact that important class of signals such as audio and images have naturally spar reprentations with respect to fixed bas, or concatenations of such bas. Moreover, efficient and provably effective algorithms bad on convex optimization or greedy pursuit are available for computing such reprentations with high fidelity.
While the success in classical signal processing applications are inspiring, in computer vision we are often more interested in the content or mantics of an image rather than a compact, high-fidelity reprentation. One might justifiably wonder, then, whether spar reprentation can be uful at all for vision tasks. The answer has been largely positive: in the past few years, variations and extensions of minimization have been applied to many vision tasks.sle
japanegirlswet 16The ability of spar reprentations to uncover mantic information derives in part from a simple but important property of the data: although the images are naturally very high dimensional, in many applications images belonging to the same class exhibit degenerate structure. That is, they lie on or near low-dimensional subspaces, or stratificat
ions. If a collection of reprentative samples are found for the distribution, we should expect that a typical sample have a very spar reprentation with respect to such a basis.
However, to successfully apply spar reprentation to computer vision tasks, we typically have to address the additional problem of how to correctly choo the basis for reprenting the data. This is different from the conventional tting in signal processing where a given basis with good property (such as being sufficiently incoherent) can be assumed. In computer vision, we often have to learn from given sample images a task-specific (often over complete) dictionary; or we have to work with one that is not necessarily incoherent. As a result, we need to extend the existing theory and algorithms for spar reprentation to new scenarios.
Automatic face recognition remains one of the most visible and challenging application domains of computer vision . Foundational results in the theory of spar reprentation have recently inspired significant progress on this difficult problem.
The key idea is a judicious choice of dictionary: reprenting the test signal as a spar linear combination of the training signals themlves. We will first e how this approach leads to simple and surprisingly effective solutions to face recognition. In turn, the face recognition example reveals new theoretical phenomena in spar reprentation that may em surprising in light of prior results.
The previous ctions examined applications in vision and machine learning in which a spar reprentation in an over complete dictionary consisting of the samples themlves yielded mantic information. For many applications, however, rather than simply using the data themlves, it is desirable to u a compact dictionary that is obtained from the data by optimizing some task-specific objective function. This ction provides an overview of approaches to learning such dictionaries, as well as their applications in computer vision and image processing.
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