Content-Based Image Retrieval Via Vector Quantization

2005 ◽  
pp. 502-509 ◽  
Author(s):  
Ajay H. Daptardar ◽  
James A. Storer
Keyword(s):  
Image Retrieval ◽  
Vector Quantization ◽  
Content Based Image Retrieval

scholarly journals CONTENT-BASED IMAGE RETRIEVAL UNTUK PENCARIAN PRODUK PONSEL

2019 ◽  
Vol 3 (1) ◽  
pp. 31
Author(s):  
Nickolas Cornelius Siantar ◽  
Jaqnson Hendryli ◽  
Dyah Erny Herwindiati
Keyword(s):  
Image Retrieval ◽  
Vector Quantization ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Content Based Image Retrieval ◽  
Color Quantization ◽  
Histogram Of Oriented Gradient ◽  
Feature Extractor ◽  
Online Stores

Phone or smartphone and online shop, there is something that cannot be separated with human. There are so many type of smartphones show up in the market that people are confused on which one to get on the online stores. Smartphones recognition is done by using the Histogram of Oriented Gradient to recognize shapes of phones, Color Quantization to recognize the color, and Local Binary Pattern to recognize texture of the phones. The output of the Feature Extractor is a feature vector which is used on the LVQ to process recognize through finding the smallest Euclidean Distance between the trained vectors. The result of this paper is an application that can recognize 16 phone types using the image with the accuracy of 9.6%. Pada saat ini, ponsel dan toko online merupakan sesuatu yang tidak dapat dipisahkan dari manusia. Begitu banyak jenis ponsel bermunculan setiap tahunnya sehingga menyebabkan manusia bingung dalam mengenali ponsel tersebut. Pada program pengenalan ponsel ini digunakan Histogram of Oriented Gradient untuk mengambil fitur berupa bentuk ponsel, Color Quantization untuk mengambil fitur warna, dan Local Binary Pattern untuk mengambil fitur tekstur ponsel. Hasil dari pengambilan fitur berupa fitur vektor yang digunakan pada Learning Vector Quantization untuk proses pengenalan dengan mencari nilai terkecil Euclidean Distance antara vektor fitur dengan vektor bobot terlatih. Hasil dari program pengenalan ini yaitu program dapat melakukan pengenalan terhadap 16 jenis ponsel dengan akurasi sebesar 9.6%.

FIXED CONTENT BASED IMAGE RETRIEVAL TECHNIQUE

2017 ◽  
Vol 5 (3) ◽  
pp. 54
Author(s):  
MOHAMMED ILIAS SHAIK ◽  
CHAUHAN DINESH ◽  
ESAPALLI SRINIVAS ◽  
PADIGE VINEETH ◽  
◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Technique
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