Density-based clustering techniques for object detection and peak segmentation in expanding data fields

Lücken, Volker; Ascheid, Gerd

doi:10.1016/b978-0-12-816637-6.00003-8

Cited by 1 publication

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“…According to the run time presented in Table 1, utilizing the two algorithms (DBSCAN and minimum distance) with SOM reduces the time spent for each operation, and DBSCAN is better than minimum distance [22][23][24][25], as shown in Figure 5.…”

Section: Resultsmentioning

confidence: 98%

A Double Clustering Approach for Color Image Segmentation

Abdulsahib

Kamaruddin

Jabar

2023

Wireless Communications and Mobile Computing

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One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first method used the minimum distance, and the second method used the clustering algorithm called DBSCAN. Both methods were tested with and without reclustering using the self-organizing map (SOM). The result from comparing the images after segmenting them and comparing the time taken to implement the segmentation process shows the effectiveness of these methods when used with SOM.

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Section: Resultsmentioning

confidence: 98%