scholarly journals Studi Akurasi Karakteristik Retina sebagai Future Identification dengan Euclidean Distance Metrics

2021 ◽  
Vol 16 (1) ◽  
pp. 19
Author(s):  
Suhendro Yusuf Irianto ◽  
Ribut Yulianto ◽  
Sri Karnila ◽  
Dona Yuliawati
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Distance Metrics

Penelitian ini menghasilkan sistem keamanan menggunakan biometrik, dengan menggunakan retina sebagai identitas pengenalan yang akurat, serta efektif untuk meningkatkan proses identifikasi pada retina dimasa depan (future identification). Hal ini sangat penting untuk menentukan keakuratan sifat biometrik apa yang paling baik di dalam proses mengidentifikasi di masa depan, sekaligus membangun suatu sistem aplikasi atau tools yang dapat digunakan untuk mengetahui karakteristik distance meterics untuk mengukur akurasi retina sebagai identitas dimasa depan (future identification). Penggunaan retina dapat menjadi salah satu alternatif identifikasi manusia  seperti  untuk  pengganti  PIN  ATM  Bank,  Paspor  dan bidang-bidang lain yang memerlukan tingkat keamanan tinggi atau mustahil untuk dapat dipalsukan. Hasil dari penelitian ini ialah berbentuk pengujian untuk membuktikan tingkat akurasi CBIR dengan menggunakan citra query dengan dibangun database sebanyak 5.000 citra retina. Metode yang akan digunakan dalam menentukan similarity dan identification dengan menggunakan fitur warna. Histogram warna untuk pencarian citra dikerjakan dengan mengitung jumlah koefisien DCT dari setiap warna. Hasil penelitian menunjukan bahwa akurasi algoritma mendekati nilai 90%, akurasi ini cukup bagus di bidang image retrieval.  Di lihat dari kecepatan proses retrieval juga cukup cepat dimana rata –rata kecepatan proses dengan menggunakan 2.000 citra digital adalah kurang dari 10 detik.

Enhancing Data Quality at ETL Stage of Data Warehousing

2021 ◽  
Vol 17 (1) ◽  
pp. 74-91
Author(s):  
Neha Gupta ◽  
Sakshi Jolly
Keyword(s):  
Data Quality ◽  
Data Warehouse ◽  
Expectation Maximization ◽  
Euclidean Distance ◽  
Missing Values ◽  
Expectation Maximization Algorithm ◽  
Distance Metrics ◽  
Multiple Sources ◽  
Minkowski Distance ◽  
Data Quality Management

Data usually comes into data warehouses from multiple sources having different formats and are specifically categorized into three groups (i.e., structured, semi-structured, and unstructured). Various data mining technologies are used to collect, refine, and analyze the data which further leads to the problem of data quality management. Data purgation occurs when the data is subject to ETL methodology in order to maintain and improve the data quality. The data may contain unnecessary information and may have inappropriate symbols which can be defined as dummy values, cryptic values, or missing values. The present work has improved the expectation-maximization algorithm with dot product to handle cryptic data, DBSCAN method with Gower metrics to ensure dummy values, Wards algorithm with Minkowski distance to improve the results of contradicting data and K-means algorithm along with Euclidean distance metrics to handle missing values in a dataset. These distance metrics have improved the data quality and also helped in providing consistent data to be loaded into a data warehouse.

scholarly journals Aplikasi Pengenalan Batu Akik Berbasis Android

2018 ◽  
pp. 10
Author(s):  
Yogiswara Dharma Putra ◽  
I Ketut Gede Darma Putra ◽  
I Putu Agus Eka Pratama
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Gabor Filter

Batu akik merupakan sebuah mineral alamiah dari prosedur geologi yang memiliki komponen kimiawi. Pemakaian perhiasan yang menggunakan batu akik popular di kalangan masyarakat umum karena keindahan dan keunikannya yang juga menjadi salah satu daya tarik bagi peminat batu akik. Penelitian Aplikasi Pengenalan Batu Akik Berbasis Android merupakan sistem image retrieval berbasis Android yang dirancang mampu memberikan kemudahan bagi peminat batu akik untuk mencari informasi dengan cepat mengenai batu akik, salah satunya adalah untuk mencari jenis-jenis batu akik. Pencarian jenis-jenis batu akik pada aplikasi dilakukan dengan Metode HSV dan YCbCr menggunakan histogram warna untuk mengenali warna, Gabor Filter untuk mengenali tekstur dan perhitungan Euclidean Distance untuk mencari kecocokan dari citra query dengan citra database. Uji coba dan analisa pada fitur warna HSV dan YCbCr menghasilkan akurasi sebesar 60,4% dan 59,6%, sedangkan Fitur tekstur Filter Gabor mendapatkan hasil akurasi pada theta 0 derajat sebesar 46,8%, theta 45 derajat sebesar 45,6%, theta 90 derajat sebesar 45.2% dan theta 135 derajat sebesar 42,8%. Kombinasi deteksi Metode HSV menggunakan histogram warna dan Filter Gabor mendapatkan akurasi lebih baik yaitu sebesar 60% dibandingkan Metode YCbCr menggunakan histogram warna dan Filter Gabor dengan hasil akurasi sebesar 56%.

Image Retrieval of Self-Adapt Distance Measure Based on SLLE

2014 ◽  
Vol 989-994 ◽  
pp. 3675-3678
Author(s):  
Xiao Fen Wang ◽  
Hai Na Zhang ◽  
Xiu Rong Qiu ◽  
Jiang Ping Song ◽  
Ke Xin Zhang
Keyword(s):  
Image Retrieval ◽  
High Dimension ◽  
Topological Structure ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Content Based Image Retrieval ◽  
Locally Linear Embedding ◽  
Euclidean Distance Measure ◽  
Linear Embedding ◽  
Dimension Space

Self-adapt distance measure supervised locally linear embedding solves the problem that Euclidean distance measure can not apart from samples in content-based image retrieval. This method uses discriminative distance measure to construct k-NN and effectively keeps its topological structure in high dimension space, meanwhile it broadens interval of samples and strengthens the ability of classifying. Experiment results show the ADM-SLLE date-reducing-dimension method speeds up the image retrieval and acquires high accurate rate in retrieval.

Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method

2010 ◽  
Vol 14 (1) ◽  
pp. 34-38 ◽  
Author(s):  
M. Rahmat Widyanto ◽  
◽  
Tatik Maftukhah ◽  
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Euclidean Distance ◽  
Retrieval System ◽  
User Feedback ◽  
Image Similarity ◽  
Modification Method ◽  
Image Retrieval System ◽  
Membership Value ◽  
Image Computation

Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.

The Technique of Shape-Based Multi-Feature Combination of TradeMark Image Retrieval

2012 ◽  
Vol 429 ◽  
pp. 287-291 ◽  
Author(s):  
Cong Zhang ◽  
Fu Cheng You
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Region Growing ◽  
Local Information ◽  
Feature Combination ◽  
Single Feature ◽  
Hu Moments ◽  
Global Matching ◽  
Better Than

At present, the technique of trademark image retrieval based on multi-feature combination of the shape mainly includes single-feature global matching or local matching and multi-feature matching, which is playing a more and more important role in the area of the trademark image retrieval. In this paper, due to the deficiency described by some single shape-based features, the technique of the multi-feature combination trademark image retrieval is proposed based on the region and the edge of a shape. Firstly, a trademark image is segmented with region growing, then low order Hu moments and eccentricity are extracted on the resulting region, which is able to express the local information of the image; Secondly, there is an extraction of Compactness and Convexity, which describe the global feature of the image, on the edge extracted with Canny. At last, the combination of the multi-feature is applied to get a Euclidean distance. Good results have been obtained in the following experiment, which proves the multi-feature combination way is better than other single-feature ways.

scholarly journals Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

2021 ◽  
Vol 38 (6) ◽  
pp. 1843-1851
Author(s):  
Ouarda Soltani ◽  
Souad Benabdelkader
Keyword(s):  
Euclidean Distance ◽  
Image Database ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
Detection Rates ◽  
Skin Segmentation ◽  
University Of Texas ◽  
Important Means ◽  
Simulation Results ◽  
Skin Samples

The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

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