机器学习在图像分割中的应用 -计算机科学与技术专业

摘要

图像分割作为计算机视觉领域的核心任务，旨在将图像划分为多个具有语义意义的区域。随着机器学习技术的发展，特别是深度学习算法的突破，图像分割研究取得了显著进展。本文聚焦于机器学习在图像分割中的应用，系统探讨了从传统机器学习方法到现代深度学习模型的技术演进。研究基于卷积神经网络（CNN）架构，提出了一种融合多尺度特征提取与上下文信息感知机制的新型分割框架，有效解决了复杂场景下目标边界模糊及小目标检测困难的问题。实验采用公开数据集进行验证，结果表明该方法在精度和效率方面均优于现有主流算法，特别是在医学影像、遥感图像等专业领域展现了优异性能。创新点在于引入注意力机制增强关键区域识别能力，并通过自适应权重调整优化不同尺度特征融合效果。此外，针对实际应用场景中计算资源受限问题，设计了轻量化网络结构，在保证分割质量的同时降低了运算成本。研究表明，所提出的机器学习驱动的图像分割方案不仅提升了分割准确性，还为跨领域应用提供了新的思路和技术支持，对推动图像处理技术发展具有重要意义。

关键词：图像分割深度学习卷积神经网络

Abstract

Image segmentation, as a core task in computer vision, aims to partition an image into multiple regions with semantic significance. With the advancement of machine learning technologies, particularly the breakthroughs in deep learning algorithms, significant progress has been made in image segmentation research. This paper focuses on the application of machine learning in image segmentation, systematically exploring the technical evolution from traditional machine learning methods to modern deep learning models. Based on the convolutional neural network (CNN) architecture, this study proposes a novel segmentation fr amework that integrates multi-scale feature extraction with context-aware mechanisms, effectively addressing issues such as ambiguous ob ject boundaries and difficulties in detecting small ob jects in complex scenes. Experiments conducted using public datasets demonstrate that this method outperforms existing mainstream algorithms in terms of both accuracy and efficiency, especially in specialized fields like medical imaging and remote sensing imagery. The innovation lies in the introduction of attention mechanisms to enhance the recognition capability of key areas and adaptive weight adjustments to optimize the fusion effect of features at different scales. Additionally, considering the limited computational resources in practical application scenarios, a lightweight network structure is designed to reduce computational costs while maintaining segmentation quality. The research indicates that the proposed machine-learning-driven image segmentation approach not only improves segmentation accuracy but also provides new ideas and technical support for cross-domain applications, playing a significant role in advancing image processing technology.

Keyword:Image Segmentation Deep Learning Convolutional Neural Network

1绪论 1

1.1机器学习与图像分割的背景意义 1

1.2国内外研究现状综述 1

1.3本文研究方法与创新点 2

2图像分割中的机器学习算法 2

2.1常用机器学习算法概述 2

2.2算法在图像分割中的应用特点 3

2.3不同算法性能对比分析 3

3数据集构建与特征提取 4

3.1图像数据集的构建方法 4

3.2特征选择与提取技术 4