Topic
Representation (systemics)
About: Representation (systemics) is a research topic. Over the lifetime, 33821 publications have been published within this topic receiving 475461 citations.
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Proceedings Article•
21 Jun 2010TL;DR: Both theoretical and experimental results show that low-rank representation is a promising tool for subspace segmentation from corrupted data.
Abstract: We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowest-rank representation among all the candidates that represent all vectors as the linear combination of the bases in a dictionary. Unlike the well-known sparse representation (SR), which computes the sparsest representation of each data vector individually, LRR aims at finding the lowest-rank representation of a collection of vectors jointly. LRR better captures the global structure of data, giving a more effective tool for robust subspace segmentation from corrupted data. Both theoretical and experimental results show that LRR is a promising tool for subspace segmentation.
1,542 citations
TL;DR: The primary problem dealt with in this paper is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.
Abstract: The primary problem dealt with in this paper is the following. Given some description of a visual object, find that object in an actual photograph. Part of the solution to this problem is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.
1,536 citations
Patent•
14 Jun 2016
TL;DR: Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation as discussed by the authors, which is the basis for the representation presented in this paper.
Abstract: Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.
1,500 citations
TL;DR: Functional neuroimaging of the human brain indicates that information about salient properties of an object is stored in sensory and motor systems active when that information was acquired, suggesting that object concepts are not explicitly represented, but rather emerge from weighted activity within property-based brain regions.
Abstract: Evidence from functional neuroimaging of the human brain indicates that information about salient properties of an object—such as what it looks like, how it moves, and how it is used—is stored in sensory and motor systems active when that information was acquired. As a result, object concepts belonging to different categories like animals and tools are represented in partially distinct, sensory- and motor property–based neural networks. This suggests that object concepts are not explicitly represented, but rather emerge from weighted activity within property-based brain regions. However, some property-based regions seem to show a categorical organization, thus providing evidence consistent with category-based, domain-specific formulations as well.
1,459 citations
TL;DR: A general, trainable system for object detection in unconstrained, cluttered scenes that derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform.
Abstract: This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform. This example-based learning approach implicitly derives a model of an object class by training a support vector machine classifier using a large set of positive and negative examples. We present results on face, people, and car detection tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver assistance system.
1,436 citations