Seed picking crossover optimisation algorithm for semantic segmentation from images

• Main Authors: Manonmani Arunkumar, Vijayakumari Pushparaj
• Format: Article
• Language: English
• Published: Wiley 2020-09-01
• Series: IET Image Processing
• Subjects: semantic image segmentation; object recognition; visually uniform regions; partitioned regions; pixel classification; segmented regions
• Online Access: https://doi.org/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.