Comparative Analysis of Similarity Methods in High-Dimensional Vectors: A Review

Yasser, Mohammad; Hussain, Khaled F.; Ali, Samia A.

doi:10.1109/caisais59399.2023.10270776

2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS) 2023

DOI: 10.1109/caisais59399.2023.10270776

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Comparative Analysis of Similarity Methods in High-Dimensional Vectors: A Review

Mohammad Yasser,

Khaled F. Hussain,

Samia A. Ali

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2024

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SPA: Annotating Small Object with a Single Point in Remote Sensing Images

Zhao,

Fang,

Cao

et al. 2024

Remote Sensing

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Detecting oriented small objects is a critical task in remote sensing, but the development of high-performance deep learning-based detectors is hindered by the need for large-scale and well-annotated datasets. The high cost of creating these datasets, due to the dense and numerous distribution of small objects, significantly limits the application and development of such detectors. To address this problem, we propose a single-point-based annotation approach (SPA) based on the graph cut method. In this framework, user annotations act as the origin of positive sample points, and a similarity matrix, computed from feature maps extracted by deep learning networks, facilitates an intuitive and efficient annotation process for building graph elements. Utilizing the Maximum Flow algorithm, SPA derives positive sample regions from these points and generates oriented bounding boxes (OBBOXs). Experimental results demonstrate the effectiveness of SPA, with at least a 50% improvement in annotation efficiency. Furthermore, the intersection-over-union (IoU) metric of our OBBOX is 3.6% higher than existing methods such as the “Segment Anything Model”. When applied in training, the model annotated with SPA shows a 4.7% higher mean average precision (mAP) compared to models using traditional annotation methods. These results confirm the technical advantages and practical impact of SPA in advancing small object detection in remote sensing.

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SPA: Annotating Small Object with a Single Point in Remote Sensing Images

Zhao,

Fang,

Cao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

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Comparative Analysis of Similarity Methods in High-Dimensional Vectors: A Review

Cited by 1 publication

References 16 publications

SPA: Annotating Small Object with a Single Point in Remote Sensing Images

SPA: Annotating Small Object with a Single Point in Remote Sensing Images

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