Utilizing multiscale local binary pattern for content-based image retrieval

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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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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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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Boosting local binary pattern with bag-of-filters for content based image retrieval

2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON) ◽

10.1109/upcon.2015.7456703 ◽

2015 ◽

Cited By ~ 8

Author(s):

Shiv Ram Dubey ◽

Satish Kumar Singh ◽

Rajat Kumar Singh

Keyword(s):

Image Retrieval ◽

Local Binary Pattern ◽

Content Based Image Retrieval

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Content-based image retrieval using local texture features in distributed environment

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319410017 ◽

2019 ◽

Vol 18 (01) ◽

pp. 1941001

Author(s):

U. S. N. Raju ◽

K. Suresh Kumar ◽

Pulkesh Haran ◽

Ramya Sree Boppana ◽

Niraj Kumar

Keyword(s):

Image Retrieval ◽

Local Binary Pattern ◽

Feature Vector ◽

Texture Features ◽

Content Based Image Retrieval ◽

Distributed Environment ◽

Average Precision ◽

Neighboring Pixel ◽

Hadoop Cluster ◽

The Relationship

In this paper, we propose novel content-based image retrieval (CBIR) algorithms using Local Octa Patterns (LOtP), Local Hexadeca Patterns (LHdP) and Direction Encoded Local Binary Pattern (DELBP). LOtP and LHdP encode the relationship between center pixel and its neighbors based on the pixels’ direction obtained by considering the horizontal, vertical and diagonal pixels for derivative calculations. In DELBP, direction of a referenced pixel is determined by considering every neighboring pixel for derivative calculations which results in 256 directions. For this resultant direction encoded image, we have obtained LBP which is considered as feature vector. The proposed method’s performance is compared to that of Local Tetra Patterns (LTrP) using benchmark image databases viz., Corel 1000 (DB1) and Brodatz textures (DB2). Performance analysis shows that LOtP improves the average precision from 59.31% to 64.36% on DB1, and from 83.24% to 85.95% on DB2, LHdP improves it to 65.82% on DB1 and to 87.49% on DB2 and DELBP improves it to 60.35% on DB1 and to 86.12% on DB2 as compared to that of LTrP. Also, DELBP reduces the feature vector length by 66.62% as compared to that of LTrP. To reduce the retrieval time, the proposed algorithms are implemented on a Hadoop cluster consisting of 116 nodes and tested using Corel 10K (DB3), Mirflickr 100,000 (DB4) and ImageNet 511,380 (DB5) databases.

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