A Double Clustering Approach for Color Image Segmentation

Abstract

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.

Segmentasi Citra Menggunakan Metode Otsu dalam Pengenalan Pola Sederhana

“…Kualitas dari hasil segmentasi memiliki dampak langsung terhadap hasil pengolahan gambar selanjutnya [13], sedangkan segmentasi gambar adalah proses penting dalam visi komputer modern. Ini termasuk mempartisi gambar menjadi bagian-bagian tertentu [14]. Tujuan dari segmentasi citra adalah untuk memisahkan objek dengan latar belakang objek tersebut melalui tahapantahapan sebagai berikut :…”