神经形态计算尖端神经网络边缘检测模型用于基于内容的图像检索
1Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Network (Bristol, England)
|May 6, 2024
概括
本研究介绍了一种生物启发的尖端神经网络 (SNN),用于基于内容的图像检索 (CBIR) 中的边缘检测. 新型SNN方法提高了CBIR性能,平均精度提高了3%以上,计算成本降低.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 基于内容的图像检索 (CBIR) 通常使用线性边缘检测方法.
- 现有的CBIR技术通常依赖于传统的基于梯度和基于导数的边缘检测.
- 在CBIR中需要更高效和有效的边缘检测.
研究的目的:
- 将基于生物启发的尖端神经网络 (SNN) 边缘检测集成到CBIR系统中.
- 为CBIR应用开发一个计算效率高的SNN方法.
- 评估CBIR的性能改进,使用拟议的基于SNN的边缘检测.
主要方法:
- 开发了一种新的,计算效率高的尖端神经网络 (SNN) 用于边缘检测.
- 将拟议的基于SNN的边缘检测集成到三个传统的CBIR技术中 (Sobel,Canny,图像衍生).
- 使用Corel-10k和作物杂草数据集评估了这种方法.
主要成果:
- 与现有的SNN模型相比,拟议的SNN方法将计算开销降低了2.5倍.
- 集成基于SNN的边缘检测的CBIR方法显示,平均精度值的平均增加超过3%.
- 基于SNN的边缘检测优化了边缘中心CBIR的特征提取.
结论:
- 拟议的基于SNN的边缘检测是增强CBIR系统的可行和有效方法.
- 这种生物启发的方法为图像检索提供了显著的效率和性能改进.
- 该研究强调了SNN在推进以边缘为中心的CBIR方法学的潜力.
相关概念视频
01:26Visual System
574
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Once through the pupil, the light passes through the lens, a...
574
01:24Vision
53.2K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.2K