Object recognition in underwater images becomes a challenging task because of its poor visibility conditions. Marine scientists often prefer automation tools for object recognition as large amount of data is captured everyday with the help of autonomous underwater vehicles. The challenge for classification in such underwater images is the limited color information. An attempt is made to recognize objects in underwater images using an adaptive Gaussian mixture model. The Gaussian mixture model performs accurate object segmentation provided the number of clusters is predefined. Optimization techniques like genetic algorithm, particle swarm optimization and differential evolution were analyzed for initializing the parameter set. Differential evolution is known for its accurate decision making in fewer iterations and proved to be better for initializing the number of clusters for the Gaussian mixture model. Further for object recognition, inner distance shape matching technique was applied. The proposed classification method achieved a maximum accuracy of 99%.

中文翻译：

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基于进化的高斯混合模型和形状匹配的水下图像智能目标识别

水下图像中的物体识别由于其较差的能见度条件而成为一项具有挑战性的任务。海洋科学家通常更喜欢使用自动化工具进行物体识别，因为每天都会在自动水下航行器的帮助下捕获大量数据。这种水下图像分类的挑战是有限的颜色信息。尝试使用自适应高斯混合模型识别水下图像中的对象。高斯混合模型执行准确的对象分割，前提是集群的数量是预定义的。分析了遗传算法、粒子群优化和差分进化等优化技术来初始化参数集。差分进化以其在较少迭代中的准确决策而闻名，并且被证明更适合初始化高斯混合模型的集群数量。此外，对于物体识别，应用了内距离形状匹配技术。所提出的分类方法达到了 99% 的最高准确率。