Motion segmentation with occlusions on the superpixel graph

Ayvaci, Alper; Soatto, Stefano

doi:10.1109/iccvw.2009.5457630

Cited by 29 publications

(16 citation statements)

References 32 publications

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“…QS08 does not allow for explicit control over the size or number of superpixels. Previous works have used QS08 for object localization [9] and motion segmentation [2].…”

Section: B Gradient-ascent-based Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

Achanta

Shaji

Smith³

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

8,162

5,057

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Abstract-Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

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“…QS08 does not allow for explicit control over the size or number of superpixels. Previous works have used QS08 for object localization [9] and motion segmentation [2].…”

Section: B Gradient-ascent-based Algorithmsmentioning

confidence: 99%

“…By default, the only parameter of the algorithm is k, the desired number of approximately equally-sized superpixels. 2 For color images in the CIELAB color space, the clustering procedure begins with an initialization step where k initial cluster centers…”

Section: A Algorithmmentioning

confidence: 99%

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

Achanta

Shaji

Smith³

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

8,162

5,057

View full text Add to dashboard Cite

show abstract

“…Superpixels can capture image redundancy and transform pixel-level computing into a region-level operation [3], which can greatly reduce the complexity of subsequent image processing tasks. Superpixel segmentation has become an important pre-processing step in various image processing applications, such as image segmentation [4][5][6][7][8][9], saliency detection [10][11][12] and classification [13,14].…”

Section: Introductionmentioning

confidence: 99%

BSLIC: SLIC Superpixels Based on Boundary Term

et al. 2017

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A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error.

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“…This property is only weakly guaranteed, but we can nevertheless exploit it in the Markov random field (MRF). While there are segmentation approaches based on superpixels [3,10], we are unaware of superpixels being used in a post-processing framework.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Improving foreground segmentations with probabilistic superpixel Markov random fields

Schick

Bauml

Stiefelhagen

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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We propose a novel post-processing framework to improve foreground segmentations with the use of Probabilistic Superpixel Markov Random Fields. First, we convert a given pixel-based segmentation into a probabilistic superpixel representation. Based on these probabilistic superpixels, a Markov random field exploits structural information and similarities to improve the segmentation. We evaluate our approach on all categories of the Change Detection 2012 dataset. Our approach improves all performance measures simultaneously for eight different basis foreground segmentation algorithms.

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Motion segmentation with occlusions on the superpixel graph

Cited by 29 publications

References 32 publications

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

BSLIC: SLIC Superpixels Based on Boundary Term

Improving foreground segmentations with probabilistic superpixel Markov random fields

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