关于探索原始的多样性和在野外识别纹理的属性
IEEE transactions on pattern analysis and machine intelligence
|October 17, 2023
概括
这项研究引入了一个新的多个原始和属性感知网络 (MPAP) 用于纹理识别. MPAP有效地模拟结构和属性关系,在准确性和效率方面超过现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 由于不同的空间背景和原始/属性感知,纹理识别是复杂的.
- 当前的卷积神经网络 (CNN) 方法往往忽略了固有的结构关系和语义属性概念,这些概念对于纹理表示至关重要.
研究的目的:
- 提出一个新的多个原始和属性感知网络 (MPAP) 进行增强的纹理表示.
- 在一个统一的框架内,模拟自下而上的结构信息和自上而下的属性感知之间的关系.
主要方法:
- 开发了一个多部门框架,整合了自下而上的和自上而下的流程.
- 底层向上流程利用结构依赖性和原始关系的空间秩序.
- 上向下流程模型通过特征转移赋予关系,并包括一个增强模块来弥合特征级别.
主要成果:
- MPAP成功地模拟了固有的结构-原始和属性-属性关系.
- 网络通过相互加强的自下而上的和自上而下的过程来学习表示.
- 与最先进的方法相比,在六个具有挑战性的纹理数据集上展示了卓越的准确性,稳定性和效率.
结论:
- 拟议的MPAP网络在纹理识别方面取得了重大进展.
- 联合建模结构和属性关系为改善纹理表示提供了关键线索.
- MPAP为纹理识别性能建立了一个新的基准.
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