Seed picking crossover optimisation algorithm for semantic segmentation from images

• Published in: IET image processing 2020-09, Vol.14 (11), p.2503-2511
• Main Authors: Arunkumar, Manonmani, Pushparaj, Vijayakumari
• Format: Article
• Language: English
• Subjects: conditional random field; CRF; Dirichlet process mixture model; high‐level contextual cues; image classification; image representation; image segmentation; iterative methods; learning (artificial intelligence); linear iterative clustering algorithm; low‐level features; minimum classification accuracy; MSRC‐21 dataset; nature‐inspired meta‐heuristic optimisation algorithm; object recognition; optimisation; optimised CRFs; parametric optimisation; partitioned regions; pattern clustering; pixel association; pixel classification; pixel transformation; Research Article; seed picking crossover optimisation algorithm; segmented regions; semantic image segmentation; semantic labels; semisegmentation process; simple linear iterative clustering algorithm; SPCO algorithm; visually uniform regions
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2019.1189

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.