FFENet:用于服装分类的频率空间特征增强网络.
Feng Yu1,2, Huiyin Li1, Yankang Shi1
1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Jiangxia District, China.
PeerJ. Computer science
|October 9, 2023
概括
本研究引入了一种使用频率空间域转换的新型服装分类网络,以改善复杂场景中的特征提取. 该方法提高了现实世界服装分析的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 服装分类在计算机视觉中至关重要,但受到复杂的现实世界场景的挑战.
- 由于复杂环境的干扰,现有的方法在特征提取方面扎,影响着轮和纹理分析.
- 糟糕的分类结果源于仅依赖杂乱的服装数据集中的空间信息.
研究的目的:
- 提出一个服装分类网络,有效地整合频率和空间域信息.
- 通过利用频率和空间数据来增强服装特征的提取,而无需频道压缩.
- 提高服装分类的准确性,特别是在复杂的现实场景中.
主要方法:
- 开发了一种基于频率空间域转换的新型网络架构,用于服装分类.
- 集成频域特征与空间域特征,保持未压缩的特征地图通道.
- 引入了一个频域特征增强模块,用于初步的服装特征提取.
- 创建并使用一个新的数据集,服装-8,专门用于复杂场景中的服装分析.
主要成果:
- 在具有挑战性的服装-8数据集上实现了93.4%的top-1模型准确度.
- 在时尚-MNIST数据集上达到94.62%的高top-1准确度.
- 在DeepFashion数据集上表现出卓越的表现,达到前三名和前五名指标基准.
结论:
- 拟议的频率空间域转换网络有效地增强了服装特征提取.
- 集成频率和空间信息显著提高了复杂场景中的服装分类准确性.
- 该网络在各种服装数据集中显示出强大的概括能力,包括现实世界的场景.
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