@inproceedings{45d5469728e04e199ec1385ef6b09dea,
title = "Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation",
abstract = "Image segmentation is the process to divide a digital image into a number of regions for further usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.",
keywords = "Computer Vision, DBSCAN, Image Segmentation, SOM",
author = "Qi Chen and Yuen, {Kevin Kam Fung} and Chun Guan",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 10th International Conference on Developments in eSystems Engineering, DeSE 2017 ; Conference date: 14-06-2017 Through 16-06-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/DeSE.2017.24",
language = "English",
series = "Proceedings - International Conference on Developments in eSystems Engineering, DeSE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "238--241",
editor = "Hani Hamdan and Dhiya Al-Jumeily and Abir Hussain and Hissam Tawfik and Jade Hind",
booktitle = "Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017",
}