A Simple and Efficient Approach for Extracting Object Hierarchy in Image Data

• Published in: International journal of advanced computer science & applications Vol. 15; no. 8
• Main Authors: Soeng, Saravit, Kong, Vungsovanreach, Thon, Munirot, Cho, Wan-Sup, Kim, Tae-Kyung
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
• Subjects: Accuracy; Algorithms; Big Data; C plus plus; Computer science; Computer vision; Deep learning; Hierarchies; Image enhancement; Machine learning; Methods; Motorcycles; Object recognition; Parent-child relations; Programming languages; Python; Source code; Telematics
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2024.01508120

An object hierarchy in images refers to the structured relationship between objects, where parent objects have one or more child objects. This hierarchical structure is useful in various computer vision applications, such as detecting motorcycle riders without helmets or identifying individuals carrying illegal items in restricted areas. However, extracting object hierarchies from images is challenging without advanced techniques like machine learning or deep learning. In this paper, a simple and efficient method is proposed for extracting object hierarchies in images based on object detection results. This method is implemented in a standalone package compatible with both Python and C++ programming languages. The package generates object hierarchies from detection results by using bounding box overlap to identify parent-child relationships. Experimental results show that the proposed method accurately extracts object hierarchies from images, providing a practical tool to enhance object detection capabilities. The source code for this approach is available at https://github.com/saravit-soeng/HiExtract.