Improved simple linear iterative clustering superpixels

Kim, Kwang-Shik; Zhang, Dongni; Kang, Mun-Cheon; Ko, Sung-Jea

doi:10.1109/isce.2013.6570216

Cited by 37 publications

(19 citation statements)

References 6 publications

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“…While the above algorithms represent a large part of the proposed superpixel algorithms, some algorithms are not included due to missing, unnoticed or only recently published implementations 2 . These include [85,86,87,88,89,90,91,92,93,94,95,96,97,98,95,99,100].…”

Section: Further Algorithmsmentioning

confidence: 99%

Superpixels: An evaluation of the state-of-the-art

Stutz¹,

Hermans²,

Leibe³

2018

Computer Vision and Image Understanding

466

322

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Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003 [1]. By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the stateof-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/.

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Section: Further Algorithmsmentioning

confidence: 99%

Superpixels: An evaluation of the state-of-the-art

Stutz¹,

Hermans²,

Leibe³

2018

Computer Vision and Image Understanding

466

322

View full text Add to dashboard Cite

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“…SLIC clusters the pixels according to the color similarity and spatial distance of the pixels, which is an iterative clustering process. In reference [23], it is pointed out that in the SLIC superpixel segmentation of color images, some pixels will be misclassified after the first iteration. All the pixels belonging to one class will be used to update the clustering center, and these misclassified pixels will have an impact on the update process.…”

Section: Superpixel Of Improved Slicmentioning

confidence: 99%

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Wang

Shen

2020

Brain Sciences

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Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

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“…g determines the size of the superpixels, therefore, the greater value of g, the larger the superpixels. To avoid placing a center in a border or in a noisy pixel, initial cluster centers are moved to the pixel with the lowest magnitude gradient in a 3×3 neighborhood (Kim et al, 2013).…”

Section: Segmentation Not As End-itself Simple Linear Iterative Clustmentioning

confidence: 99%

“…Thus isolated pixels and small segments are merged with its larger neighbor (Kim et al, 2013). An example of applying SLIC method to an RGB image is shown in Figure 2.11, as can be observed, superpixels adhere well to the boundaries of spectrally homogeneous regions.…”

Section: Segmentation Not As End-itself Simple Linear Iterative Clustmentioning

confidence: 99%

Spatio-temporal Analysis of Agricultural Landscape Images: A Superpixel-based Approach

Pedrero¹,

Adolfo²

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In the last hundred years, the world population has tripled and is still growing dramatically but resources have remained the same, causing changes in food supply outlook. According to the Food and Agriculture Organization (FAO), global food production will need to grow by 70% in order to satisfy the food and feed demand of a population of 9 billion people by 2050. In the current scenario, with limited arable lands and water scarcity, this means a greater pressure than ever before on productive land; thus today's main challenge is to put agriculture on a more sustainable and productive long-term path. In this context, the study of the variability within plots represents an opportunity for improving agricultural management. For most farmers, agricultural plots are variable which implies that not all areas within them require the same management; for example: some areas have specific needs of water, fertilizers, or pesticides in order to be more profitable. Therefore, adjusting the management of the plots based on the variability within them opens a route to a variable farm management able to improve cost/yield relationship.New generation of optical remote sensors placed on aircraft, satellite platforms and drones, offers accessible and useful data of very-high resolution for monitoring and determining spatial variability of agricultural fields at plot level. However, the landscape complexity makes the manual analysis of the variability within an agricultural plot a highly time-consuming and expensive task. At the same time, it hinders considerably the monitoring of a high number of agricultural plots simultaneously. Moreover this very-high spatial resolution represents a challenge for traditional approaches of analysis based on pixels, unable to handle the withinclass spectral variability, intrinsic to this type of images. Therefore, it exists a critical need to develop methodologies for efficiently and automatically extracting and analyzing information from very-high resolution images, that would allow stakeholders to enable variable farm management. In this context, it is of particular interest to automatically detect region dynamics within the agricultural plots.The aim of this work is the development of a methodology for automatic generation of spatial and temporal information on the dynamics of agricultural land, particularly at plot level. In this sense, the study of agricultural scenes has been addressed through an object-based approach, exploring superpixel methods, which are seen as a link between the pixels of the image and objects of interest. To this end a superpixel method for multi-spectral images has been proposed. This method has been exploited in different applications at three different scales of interest:vii (i) to analyze fragmented agricultural scenes, (ii) to delineate agricultural plots, and (iii) to capture the internal variability of agricultural plots.The analysis of fragmented agricultural scenes has been approached by two methodologies. The first one focuses on providing a fram...

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Improved simple linear iterative clustering superpixels

Cited by 37 publications

References 6 publications

Superpixels: An evaluation of the state-of-the-art

Superpixels: An evaluation of the state-of-the-art

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Spatio-temporal Analysis of Agricultural Landscape Images: A Superpixel-based Approach

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