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Convex optimization for scene understanding
- In ICCV Workshop
, 2013
"... Abstract In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not ..."
Abstract
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Cited by 2 (2 self)
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Abstract In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on pre-processing techniques such as object detectors or superpixels. The central idea is to integrate a hierarchical label prior and a set of convex constraints into the segmentation approach, which combine the three tasks by introducing high-level scene information. Instead of learning label co-occurrences from limited benchmark training data, the hierarchical prior comes naturally with the way humans see their surroundings.
Nature
"... In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on p ..."
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In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on pre-processing techniques such as object detectors or superpixels. The central idea is to integrate a hierarchical label prior and a set of convex constraints into the segmentation approach, which combine the three tasks by introducing high-level scene information. Instead of learning label co-occurrences from limited benchmark training data, the hierarchical prior comes naturally with the way humans see their surroundings.
Communicated by Nikos Komodakis.
, 2014
"... Abstract In this article we introduce the concept of midrange geometric constraints into semantic segmentation. We call these constraints ‘midrange ’ since they are neither global constraints, which take into account all pixels without any spatial limitation, nor are they local constraints, which on ..."
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Abstract In this article we introduce the concept of midrange geometric constraints into semantic segmentation. We call these constraints ‘midrange ’ since they are neither global constraints, which take into account all pixels without any spatial limitation, nor are they local constraints, which only regard single pixels or pairwise relations. Instead, the proposed constraints allow to discourage the occurrence of labels in the vicinity of each other, e.g., ‘wolf ’ and ‘sheep’. ‘Vicinity ’ encompasses spatial distance as well as specific spatial directions simultaneously, e.g., ‘plates ’ are found directly above ‘tables’, but do not fly over them. It is up to the user to specifically define the spatial extent of the constraint between each two labels. Such constraints are not only interesting for scene segmentation, but also for part-based articulated or rigid objects. The reason is that object parts such as for example arms, torso and legs usually obey specific spatial rules, which are among the few things that remain valid for articulated objects over many images and which can be expressed in terms of the proposed midrange constraints, i.e. closeness and/or direction. We show, how midrange geometric constraints are formulated within a con-tinuous multi-label optimization framework, and we give a convex relaxation, which allows us to find globally optimal
Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation
"... Abstract. Semantic segmentation aims at jointly computing a segmen-tation and a semantic labeling of the image plane. The main ingredient is an efficient feature selection strategy. In this work we perform a sys-tematic information-theoretic evaluation of existing features in order to address the qu ..."
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Abstract. Semantic segmentation aims at jointly computing a segmen-tation and a semantic labeling of the image plane. The main ingredient is an efficient feature selection strategy. In this work we perform a sys-tematic information-theoretic evaluation of existing features in order to address the question which and how many features are appropriate for an efficient semantic segmentation. To this end, we discuss the tradeoff between relevance and redundancy and present an information-theoretic feature evaluation strategy. Subsequently, we perform a systematic ex-perimental validation which shows that the proposed feature selection strategy provides state-of-the-art semantic segmentations on five seman-tic segmentation datasets at significantly reduced runtimes. Moreover, it provides a systematic overview of which features are the most relevant for various benchmarks.
