Identification of Buni Fruit Image Using Euclidean Distance Method

Abstract

Identification is an important part of image analysis because in this procedure the desired image/image will be analyzed for further processing to make it easier to analyze for further purposes, for example in image identification pattern recognition which is part of image analysis used to divide an image into several parts and take some of the desired objects. This study aims to identify buni fruit with Euclidean distance and extract shape and texture features. Extraction of shape features using boundary metrics and whimsy. This boundary is considered to be able to recognize objects based on their shape and can distinguish them from other objects. For identification expositions, Euclidean distance is used which serves to represent the level of similarity of two images that take into account the distance value of the Euclidean distance. From the results of the evaluation using a disarray network by calculating precision, review, and accuracy, in order to identify the image of the buni fruit object properly.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

“…Moreover, Euclidean distance is the distance calculation that is most commonly used in the machine learning process. On the other hand, the Manhattan distance is the sum of the distances from all attributes [31,32,33]. In this experiment, there are four main activities in image retrieval research: collecting images, pre-processing images, training by feature extraction, and testing by finding similarities between datasets.…”