Content Based Image Retrieval Using Gray Level Co-Occurance Matrix with SVD and Local Binary Pattern

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%.

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WAVELET BASED CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL COOCURENCE MATRIX AND COLOR COOCURENCE MATRIX

Journal of Computer Science ◽

10.3844/jcssp.2014.15.22 ◽

2014 ◽

Vol 10 (1) ◽

pp. 15-22 ◽

Cited By ~ 12

Author(s):

Jeyanthi Prabhu ◽

Jawahar Senthil Kumar

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Texture Feature ◽

Content Based Image Retrieval ◽

Gray Level ◽

Texture Feature Extraction

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Online product search using gray level co-occurrence matrix, color moments, and histogram of oriented gradients for content based image retrieval

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/852/1/012140 ◽

2020 ◽

Vol 852 ◽

pp. 012140

Author(s):

Henry Halim ◽

J Hendryli ◽

D E Herwindiati

Keyword(s):

Image Retrieval ◽

Content Based Image Retrieval ◽

Gray Level ◽

Histogram Of Oriented Gradients ◽

Product Search ◽

Occurrence Matrix

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Combining Local Binary Pattern and Speeded-Up Robust Feature for Content-Based Image Retrieval

Communications in Computer and Information Science - Intelligent Information and Database Systems ◽

10.1007/978-981-15-3380-8_32 ◽

2020 ◽

pp. 366-376

Author(s):

Prashant Srivastava ◽

Manish Khare ◽

Ashish Khare

Keyword(s):

Image Retrieval ◽

Local Binary Pattern ◽

Content Based Image Retrieval

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Quaternion Polar Complex Exponential Transform and Local Binary Pattern-Based Fusion Features for Content-Based Image Retrieval

Lecture Notes in Electrical Engineering - Microelectronics, Electromagnetics and Telecommunications ◽

10.1007/978-981-15-3828-5_80 ◽

2020 ◽

pp. 769-776

Author(s):

D. Kishore ◽

Ch. Srinivasa Rao

Keyword(s):

Image Retrieval ◽

Local Binary Pattern ◽

Content Based Image Retrieval ◽

Exponential Transform ◽

Polar Complex Exponential Transform ◽

Complex Exponential ◽

Fusion Features

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Content-based image retrieval using scale invariant feature transform and gray level co-occurrence matrix

10.1117/12.2280442 ◽

2017 ◽

Cited By ~ 1

Author(s):

Prashant Srivastava ◽

Manish Khare ◽

Ashish Khare

Keyword(s):

Image Retrieval ◽

Scale Invariant Feature Transform ◽

Content Based Image Retrieval ◽

Gray Level ◽

Scale Invariant ◽

Invariant Feature ◽

Feature Transform ◽

Occurrence Matrix ◽

Scale Invariant Feature

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A Multiresolution Approach for Content-Based Image Retrieval Using Wavelet Transform of Local Binary Pattern

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-319-75420-8_50 ◽

2018 ◽

pp. 529-538 ◽

Cited By ~ 1

Author(s):

Manish Khare ◽

Prashant Srivastava ◽

Jeonghwan Gwak ◽

Ashish Khare

Keyword(s):

Wavelet Transform ◽

Image Retrieval ◽

Local Binary Pattern ◽

Content Based Image Retrieval

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Content Based Image Retrieval Berdasarkan Fitur Low Level: Literature Review

Jurnal Buana Informatika ◽

10.24002/jbi.v8i2.1077 ◽

2017 ◽

Vol 8 (2) ◽

Author(s):

Rahmad Hidayat ◽

Agus Harjoko ◽

Anny Kartika Sari

Keyword(s):

Image Retrieval ◽

Color Space ◽

Human Perception ◽

Image Features ◽

Content Based Image Retrieval ◽

Gray Level ◽

Color Feature ◽

Low Level ◽

Abstract Content ◽

Color Texture

Abstract. Content-based Image Retrieval (CBIR) is an image search process by comparing the image features sought by the images contained in the database. Low-level features in the image are commonly used in CBIR is the color, texture, and shape. This article conducts a review of journals related to CBIR, particularly research based on low-level features. The journals are then classified based on the color space, features and feature extraction methods. The results show that the color space often used is the RGB and HSV due to their compatibility with the hardware and human perception of color. The features most often used in CBIR is the color feature. This is due to the fact that color features can easily and quickly be extracted. The most often used method to extract the color feature is the color histogram, the most common method used to extract texture features is the gray level co-occurence matrix, and the method most widely used to extract the shape feature is canny edge.Keywords: CBIR, color, texture, shape. Abstract. Content based Image Retrieval (CBIR) merupakan proses pencarian gambar dengan membandingkan fitur-fitur yang terdapat pada gambar yang dicari dengan gambar yang terdapat dalam basis data. Fitur-fitur low level pada gambar yang biasa digunakan dalam CBIR adalah warna, tekstur, dan bentuk Artikel ini melakukan tinjauan terhadap penelitian-penelitian yang berkaitan dengan CBIR, khususnya penelitian yang berbasis pada fitur low level. Penelitian-penelitian tersebut kemudian diklasifikasikan berdasarkan ruang warna, fitur dan metode ekstraksi fitur. Hasil tinjauan menunjukkan bahwa ruang warna yang sering digunakan adalah RGB dan HSV karena dianggap cocok dengan hardware dan persepsi manusia terhadap warna. Adapun fitur yang paling sering digunakan dalam CBIR adalah fitur warna. Hal ini disebabkan fitur warna mudah dan cepat diekstraksi. Metode yang paling sering digunakan untuk mengekstraksi fitur warna adalah histogram warna, metode yang paling sering digunakan untuk mengekstraksi fitur tekstur adalah gray level co-occurence matrix, dan metode yang paling banyak digunakan untuk, mengekstraksi fitur bentuk adalah canny edge.Kata kunci: CBIR, warna, tekstur, bentuk.

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