&amp;#x201C;Lattice Cut&amp;#x201D; - Constructing superpixels using layer constraints

Moore, Alastair; Prince, Simon J. D.; Warrell, Jonathan

doi:10.1109/cvpr.2010.5539890

Cited by 50 publications

(40 citation statements)

References 24 publications

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“…Thus, compared to particular regular SP algorithms, it is more desirable to find a way rectifying arbitrary segmentations into a regular structure. Besides, another notable weakness of current regular SP methods is that their performance may highly depend on the pre-computed edge map [16,21]. This paper, to the best of our knowledge, for the first time proposes a generic approach to optimally regularizing arbitrary SPs into a regular grid.…”

Section: Introductionmentioning

confidence: 99%

“…SuperLattice [ 17], LatticeCut [16], TurboPixel [14] and min-energy based SPs [25]. They generally follow a similar strategy to generate SPs, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…1(c), image over-segmentations usually tend to generate SPs with variant sizes, shapes and irregular spatial dis-tributions. As the increasing usage of SPs in image parsing [22], segmentation [18,24], co-segmentation [10,20], and object localization [13], people start to realize the importance of structural regularities in SPs [1,16,21,25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maximum Cohesive Grid of Superpixels for Fast Object Localization

Feng

Wan

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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show abstract

Section: Introductionmentioning

confidence: 99%

“…SuperLattice [ 17], LatticeCut [16], TurboPixel [14] and min-energy based SPs [25]. They generally follow a similar strategy to generate SPs, i.e.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum Cohesive Grid of Superpixels for Fast Object Localization

Feng

Wan

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…Later the authors combined scene shape prior to achieve an adaptive lattice [24]. Further investigation of lattice superpixel [23] is derived from global optimization. The superpixel generation is initialized with a grid, and the graph cut algorithm is adopted to iteratively optimize the vertical and horizontal seams.…”

Section: Introductionmentioning

confidence: 99%

Structure-Sensitive Superpixels via Geodesic Distance

et al. 2012

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Over-segments (i.e. superpixels) have been commonly used as supporting regions for feature vectors and primitives to reduce computational complexity in various image analysis tasks. In this paper, we describe a structuresensitive over-segmentation technique by exploiting Lloyd's algorithm with a geodesic distance. It generates smaller superpixels to achieve lower under-segmentation in structuredense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute the geodesic distances amongst pixels, and in the segmentation procedure, the density of over-segments is automatically adjusted according to an energy functional that embeds color homogeneity, structure density and compactness constraints. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms prior arts while offering a comparable computational efficiency with fast methods, such as TurboPixels.

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“…They seek superpixels that conform to a grid, which has storage and efficiency advantages. The work in [20] is based on greedy optimization, and [21] uses a more global optimization, which, like our work, is also based on graph cuts. The formulation in [20,21] poses certain restrictions on superpixel shapes: the boundary between superpixels cannot "turn back" on itself.…”

Section: Introductionmentioning

confidence: 99%

Superpixels and Supervoxels in an Energy Optimization Framework

Veksler

Boykov

Mehrani

2010

Lecture Notes in Computer Science

320

269

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Abstract. Many methods for object recognition, segmentation, etc., rely on tessellation of an image into "superpixels". A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels can be extracted with any segmentation algorithm, however, most of them produce highly irregular superpixels, with widely varying sizes and shapes. A more regular space tessellation may be desired. We formulate the superpixel partitioning problem in an energy minimization framework, and optimize with graph cuts. Our energy function explicitly encourages regular superpixels. We explore variations of the basic energy, which allow a trade-off between a less regular tessellation but more accurate boundaries or better efficiency. Our advantage over previous work is computational efficiency, principled optimization, and applicability to 3D "supervoxel" segmentation. We achieve high boundary recall on 2D images and spatial coherence on video. We also show that compact superpixels improve accuracy on a simple application of salient object segmentation.

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“Lattice Cut” - Constructing superpixels using layer constraints

Cited by 50 publications

References 24 publications

Maximum Cohesive Grid of Superpixels for Fast Object Localization

Maximum Cohesive Grid of Superpixels for Fast Object Localization

Structure-Sensitive Superpixels via Geodesic Distance

Superpixels and Supervoxels in an Energy Optimization Framework

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