NDLI: Seed picking crossover optimisation algorithm for semantic segmentation from images

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Seed picking crossover optimisation algorithm for semantic segmentation from images

Content Provider	IET Digital Library
Abstract	Semantic image segmentation treats the issues involved in the object recognition and image segmentation as a combined task. The chief notion of semantic segmentation is to partition the image into visually uniform regions and to discriminate the class of the partitioned regions. Pixel classification is done over the segmented regions by assigning semantic labels. In general, inference frameworks are fed with the combination of low-level features and high-level contextual cues to segment an image. Since these combinations are rarely object consistent, result with minimum classification accuracy because of choosing non-influencing features and cues to track specific objects. To overcome this problem, a nature-inspired meta-heuristic optimization algorithm called Seed Picking Crossover Optimization (SPCO) is proposed to optimize i.e. train the CRF (Conditional Random Field) for choosing relevant feature to segment the object with high accuracy. To meritoriously recognize the objects, a semi-segmentation process is initially performed using Simple Linear Iterative Clustering (SLIC) algorithm. For pixel transformation and pixel association, Dirichlet process mixture model and CRF are employed. Optimized CRFs are used where the parametric optimization is done using the proposed SPCO algorithm. The proposed work results with 84% on classification accuracy and the performance evaluations are done using MSRC-21 dataset.
Starting Page	2503
Ending Page	2511
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 11, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1189
Journal	IET Image Processing
Publisher Date	2020-05-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Conditional Random Field CRF Dirichlet Process Mixture Model High-level Contextual Cues Image Classification Image Recognition Image Representation Image Segmentation Interpolation And Function Approximation Iterative Method Knowledge Engineering Technique Learning in AI Linear Iterative Clustering Algorithm Low-level Feature Minimum Classification Accuracy MSRC-21 Dataset Nature-inspired Meta-heuristic Optimisation Algorithm Numerical Analysis Object Recognition Optimisation Optimisation Technique Optimised CRFs Parametric Optimisation Partitioned Region Pattern Clustering Pixel Association Pixel Classification Pixel Transformation Seed Picking Crossover Optimisation Algorithm Segmented Region Semantic Image Segmentation Semantic Labels Semisegmentation Process Simple Linear Iterative Clustering Algorithm SPCO Algorithm Visually Uniform Region
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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