Multiple Kernel Boosting Saliency Detection of Flame Image of Sintering Section Article Swipe

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Fubin Wang , Hefei Liu , Rui Wang , Jianghong He , Chen Wu ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.3724/sp.j.1089.2021.18686

The flame image of the sintering section is usually interfered by smoke and halo, which would cause the blurring of flame edge and material layer in the image. In order to solve the problem that traditional saliency detection method based on two-dimensional image features is difficult to effectively obtain the actual saliency of the cross-sectional flame image, a saliency detection method based on boundary connectivity and multi-kernel Boosting is proposed. Firstly, the color de-correlation is used in the process of image color space conversion. Boundary connectivity and dark channel prior principle are used to obtain the initial saliency map. Secondly, the super-pixel region information, regional variance and regional contrast of the flame image are used to construct region descriptor, the multiple kernel Boosting algorithm based on support vector regression is used to generate the complementary saliency map on 4 scales. Finally, the initial saliency map and the complementary saliency map are fused to obtain the final saliency map. 600 flame images including manual labeling are used to compare the proposed method with other 5 existing methods and each stage of the proposed method is analyzed. P-R curve, F-measure, mean absolute error and running time are taken as evaluation indexes. The experimental results show that the proposed method is superior to the other 5 methods, and the detection performance of each stage is gradually enhanced, which lays a foundation for improving the effective information extraction of sintering flame section image.

Related Topics

Boosting (Machine Learning)

Artificial Intelligence

Computer Science

Pixel

Mathematics

Support Vector Machine

Computer Vision

Combinatorics

Concepts

Boosting (machine learning) Artificial intelligence Kernel (algebra) Computer science Pattern recognition (psychology) Pixel Image (mathematics) Mathematics Support vector machine Computer vision Combinatorics

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Type: article
Language: en
Landing Page: https://doi.org/10.3724/sp.j.1089.2021.18686
PDF: https://www.sciengine.com/doi/pdf/AC51AFE6AD3D4D5F82DA16960E8C68CD
OA Status: bronze
Cited By: 1
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DOI: https://doi.org/10.3724/sp.j.1089.2021.18686

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Title: Multiple Kernel Boosting Saliency Detection of Flame Image of Sintering Section

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-09-01

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Authors: Fubin Wang, Hefei Liu, Rui Wang, Jianghong He, Chen Wu

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Landing page: https://doi.org/10.3724/sp.j.1089.2021.18686

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Concepts: Boosting (machine learning), Artificial intelligence, Kernel (algebra), Computer science, Pattern recognition (psychology), Pixel, Image (mathematics), Mathematics, Support vector machine, Computer vision, Combinatorics

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Cited by: 1

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abstract_inverted_index.complementary	135, 148
abstract_inverted_index.de-correlation	74
abstract_inverted_index.indent=0mm>The	1
abstract_inverted_index.cross-sectional	55
abstract_inverted_index.two-dimensional	42
abstract_inverted_index.<italic>F</italic>-measure,	188
abstract_inverted_index.<italic>P</italic>-<italic>R</italic>	186
cited_by_percentile_year.max	95
cited_by_percentile_year.min	91
countries_distinct_count	1
institutions_distinct_count	5
citation_normalized_percentile.value	0.21621144
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False