Adaptive Centroid Placement Based SNIC for Superpixel Segmentation

Senanayaka, Janith Bandara; Morawaliyadda, Dilshan Thilanka; Senarath, Shehan Tharuka; Godaliyadda, G. M. R. I.; Ekanayake, M. P. B.

doi:10.1109/mercon50084.2020.9185361

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Then, through non-neighbor joining and the most similar superpixels, the superpixels reach the preset number of image semantic tags, and automatically segment each image The semantic area of the target greatly improves the accuracy and efficiency of subsequent image processing. Aiming at the problem that the SNIC superpixel algorithm does not take into account the information contained in the image well, Bandara et al [ 20 ] proposed a superpixel segmentation algorithm based on image information entropy. The image is divided into the information-rich area and the information-sparse area, and then the mean shift algorithm is used to generate the initial point center on the information-rich area, and the SNIC algorithm is used for segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, there have been many improvements and optimizations to the SNIC algorithm. Many methods [ 17 , 18 , 19 , 20 ] have been improved based on the original SNIC algorithm, and have been verified on various data and above, and their performance and practicability have been improved. However, many algorithms still have room for improvement in segmentation speed and segmentation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm

Zhang

et al. 2022

Tomography

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Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm

Zhang

et al. 2022

Tomography

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“…Firstly, since the CIELAB color space is not suitable for time series images, SIs time series were directly input into the SNIC, and a penalty-coefficient-based dynamic time warping (DTW) [56] algorithm was introduced to measure the distance from candidate time series (t i ) to the superpixel centroid (t k ), as given by Equation ( 6). Secondly, an adaptive centroid placement method [57] was employed to produce initial centroids according to the information distribution of the images.…”

Section: Time Series Superpixel Segmentationmentioning

confidence: 99%

Mapping Paddy Rice in Complex Landscapes with Landsat Time Series Data and Superpixel-Based Deep Learning Method

Zhang

Jin

2022

Remote Sensing

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The spatial pattern and temporal variation in paddy rice areas captured by remote sensing imagery provide an effective way of performing crop management and developing suitable agricultural policies. However, fragmented and scattered rice paddies due to undulating and varied topography, and the availability and quality of remote sensing images (e.g., frequent cloud coverage) pose significant challenges to accurate long-term rice mapping, especially for traditional pixel and phenological methods in subtropical monsoon regions. This study proposed a superpixel and deep-learning-based time series method to analyze Landsat time series data for paddy rice classification in complex landscape regions. First, a superpixel segmentation map was generated using a dynamic-time-warping-based simple non-iterative clustering algorithm with preprocessed spectral indices (SIs) time series data. Second, the SI images were overlaid onto the superpixel map to construct mean SIs time series for each superpixel. Third, a multivariate long short-term memory full convolution neural network (MLSTM-FCN) classifier was employed to learn time series features of rice paddies to produce accurate paddy rice maps. The method was evaluated using Landsat imagery from 2000 to 2020 in Cengong County, Guizhou Province, China. Results indicate that the superpixel MLSTM-FCN achieved a high performance with an overall accuracy varying from 0.9547 to 0.9721, which presents an 0.17–1.23% improvement compared to the random forest method. This study showed that combining spectral, spatial, and temporal features with deep learning methods can generate accurate paddy rice maps in complex landscape regions.

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