Algorithm of Adaptive Fast Clustering for Fish Swarm Color Image Segmentation

Fish swarm image segmentation provides an easy to understand and analyze representation for behavior monitoring feature extraction and image information analysis, accurate and effective image segmentation is the basis of fish shoal monitoring. In this paper, algorithm of adaptive fast clustering for...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 178753 - 178762
Main Authors: Sun, Longqing, Luo, Bing, Liu, Ting, Liu, Yan, Wei, Yaoguang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Algorithms
Brightness
cluster value
Clustering
Clustering algorithms
color compensation
Color imagery
color library
Data analysis
Feature extraction
Fish
Gray scale
Histograms
Hypoxia
Image color analysis
Image quality
Image segmentation
Information management
K-Means
Libraries
Monitoring
Signal to noise ratio
Statistical analysis
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Fish swarm image segmentation provides an easy to understand and analyze representation for behavior monitoring feature extraction and image information analysis, accurate and effective image segmentation is the basis of fish shoal monitoring. In this paper, algorithm of adaptive fast clustering for fish swarm color image segmentation was proposed by combining the fish swarm hypoxia image features and K-Means++ algorithm. In the RGB color space, the color information of the channel with the maximum average brightness is retained, the others compensation was zero, the generated new image replaced the original. The fish color library was constructed using the gray distribution statistics of the fish swarm targets and background. The pixel probability distribution value in each gray scale range of the normalized gray histogram of the newly generated image is calculated, and combine with the fish group target gray scale reference statistic, the clustering value is determined by two traversing. According to the reserved channel information, the corresponding cluster fish swarm color library is selected for color clustering. The clustering result is processed by threshold transformation to finally realize fish swarm image segmentation. Experiment showed that the average range of structural similarity of our algorithm was [0.93, 1], and the average range of peak signal-to-noise ratio was [44, 50], the running time of the algorithm in this paper is 56% shorter than K-Means ++ algorithm and 71% shorter than the fuzzy clustering algorithm when processing the same images, which could met the requirements of image segmentation quality and accuracy for fish behavior detection.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2956988