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Results 1 - 10
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1,547
On Applying Linear Discriminant Analysis for Multi-labeled Problems?
"... Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective ..."
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Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective
A convex formulation of continuous multi-label problems
- In ECCV, pages III: 792–805
, 2008
"... Abstract. We propose a spatially continuous formulation of Ishikawa’s discrete multi-label problem. We show that the resulting non-convex variational problem can be reformulated as a convex variational problem via embedding in a higher dimensional space. This variational problem can be interpreted a ..."
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Cited by 66 (13 self)
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Abstract. We propose a spatially continuous formulation of Ishikawa’s discrete multi-label problem. We show that the resulting non-convex variational problem can be reformulated as a convex variational problem via embedding in a higher dimensional space. This variational problem can be interpreted
Epitomized Priors for Multi-labeling Problems
"... Image parsing remains difficult due to the need to com-bine local and contextual information when labeling a scene. We approach this problem by using the epitome as a prior over label configurations. Several properties make it suited to this task. First, it allows a condensed patch-based representat ..."
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Cited by 2 (0 self)
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Image parsing remains difficult due to the need to com-bine local and contextual information when labeling a scene. We approach this problem by using the epitome as a prior over label configurations. Several properties make it suited to this task. First, it allows a condensed patch
Curvature Regularity for Multi-Label Problems- Standard and Customized Linear Programming
"... Abstract. We follow recent work by Schoenemann et al. [25] for expressing curvature regularity as a linear program. While the original formulation focused on binary segmentation, we address several multi-label problems, including segmentation, denoising and inpainting, all cast as a single linear pr ..."
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Cited by 2 (0 self)
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Abstract. We follow recent work by Schoenemann et al. [25] for expressing curvature regularity as a linear program. While the original formulation focused on binary segmentation, we address several multi-label problems, including segmentation, denoising and inpainting, all cast as a single linear
HIGH-DIMENSION MULTI-LABEL PROBLEMS: CONVEX OR NON CONVEX RELAXATION?
"... Abstract. This paper is concerned with the problem of relaxing non convex functionals, used in image processing, into convex problems. We review most of the recently introduced relaxation methods, and we propose a new convex one based on a probabilistic approach, which has the advantages of being in ..."
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to initialization. As a case of study, we illustrate our theoretical analysis with numerical experiments, namely for the optical flow problem. Key words. Multi-label problems, convex relaxation, segmentation, disparity and optical flow
Using a Probabilistic Neural Network for a Large Multi-label Problem
"... The automation of the categorization of economic activ-ities from business descriptions in free text format is a huge challenge for the Brazilian governmental administration in the present day. When this problem is tackled by humans, the subjectivity on their classification brings another prob-lem: ..."
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Cited by 1 (1 self)
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of possible categories considered is very large, more than 1000 in the Brazilian scenario. The large number of categories makes the problem even harder to be solved, as this is also a multi-labeled problem. In this work we compared the multi-label lazy learning technique, ML-KNN, to our Probabilistic Neural
Label Ranking Based Semantic Texton Forests for Multi-label Problem
"... When comprehending scenes from images such as indoor and outdoor landscape photos, it is necessary not only to segment them by color, but also to recognize regions of the same category to segment them. This is called semantic ..."
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When comprehending scenes from images such as indoor and outdoor landscape photos, it is necessary not only to segment them by color, but also to recognize regions of the same category to segment them. This is called semantic
Support Vector Machine-based approach for multi-labelers problems
"... Abstract. We propose a first approach to quantify the panelist’s labeling generalizing a soft-margin support vector machine classifier to multi-labeler analysis. Our approach consists of formulating a quadratic optimization problem instead of using a heuristic search algorithm. We determine penalty ..."
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Cited by 1 (0 self)
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Abstract. We propose a first approach to quantify the panelist’s labeling generalizing a soft-margin support vector machine classifier to multi-labeler analysis. Our approach consists of formulating a quadratic optimization problem instead of using a heuristic search algorithm. We determine penalty
What is optimized in tight convex relaxations for multi-label problems
- in Proc. CVPR
, 2012
"... In this work we present a unified view onMarkov random fields and recently proposed continuous tight convex relax-ations for multi-label assignment in the image plane. These relaxations are far less biased towards the grid geometry than Markov random fields. It turns out that the continu-ous methods ..."
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Cited by 5 (2 self)
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In this work we present a unified view onMarkov random fields and recently proposed continuous tight convex relax-ations for multi-label assignment in the image plane. These relaxations are far less biased towards the grid geometry than Markov random fields. It turns out that the continu-ous
Results 1 - 10
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