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.

中文翻译：

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用于图像语义分割的种子采摘交叉优化算法

语义图像分割将对象识别和图像分割中涉及的问题视为组合任务。语义分割的主要概念是将图像划分为视觉上均匀的区域，并区分所划分区域的类别。通过分配语义标签，在分割区域上完成像素分类。通常，将低级功能和高级上下文提示相结合来为推理框架提供细分图像。由于这些组合很少与对象保持一致，因此由于选择了无影响的特征和线索来跟踪特定对象而导致分类精度最低。为了克服这个问题，提出了一种自然启发式的元启发式优化算法，称为种子采摘交叉优化（SPCO），以优化 训练CRF（条件随机场）以选择相关特征以高精度分割对象。为了出色地识别对象，最初使用简单线性迭代聚类（SLIC）算法执行半分段过程。对于像素变换和像素关联，采用Dirichlet过程混合模型和CRF。在使用建议的SPCO算法完成参数优化的情况下，使用优化的CRF。拟议的工作结果具有84％的分类精度，并且使用MSRC-21数据集完成了性能评估。对于像素变换和像素关联，采用Dirichlet过程混合模型和CRF。在使用建议的SPCO算法完成参数优化的情况下，使用优化的CRF。拟议的工作结果具有84％的分类精度，并且使用MSRC-21数据集完成了性能评估。对于像素变换和像素关联，采用Dirichlet过程混合模型和CRF。在使用建议的SPCO算法完成参数优化的情况下，使用优化的CRF。拟议的工作结果具有84％的分类精度，并且使用MSRC-21数据集完成了性能评估。