Content-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques

Alrahhal, Maher; Supreethi, K. P.

doi:10.1109/aicai.2019.8701255

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…Similarity Measures: measure the similarity between the feature vector of the query image and all feature vectors in the feature database; which made in the offline phase of the CBIR system by repeating the feature extraction step for all the image in the database, using this equation (1) [48]:…”

Section: Methodsmentioning

confidence: 99%

“…Step 7: Local Neighbor Pattern (LNP): This step starts with converting the query image to a grayscale image and then finds the Local Neighbor Pattern (LNP) histogram [48] to get the second feature vector , whose size is 256. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…1) Methods that used specifically for texture image retrieval like LDP [56], LTrP [50], LOtP [57], LHDP [57], and LNP [48]. Those methods work on the pixels' values directly and encrypt the relationship between the center pixel and its neighbors.…”

Section: A First Experiment: Image Retrieval Using Fdlnpmentioning

confidence: 99%

“…In this experiment, we used the second way that has the following advantages, enhancing the accuracy of the CBIR as we proved in our previous work [48], reducing the number of comparisons, and make it confined in a specific class. Finally, the output of the classification-based image retrieval system always retrieves similar images, unless the classifier misclassifies the query image, which leads to output completely wrong results, and this is the main disadvantage of this way.…”

Section: Third Experiment: Image Retrieval Using Machine Learning Classificationmentioning

confidence: 99%

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Full Direction Local Neighbors Pattern (FDLNP)

Alrahhal¹,

K.P²

2021

IJACSA

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In this paper, we proposed the Full Direction Local Neighbor Pattern (FDLNP) algorithm, which is a novel method for Content-Based Image Retrieval. FDLNP consists of many steps, starting from generating Max and Min Quantizers followed by building two matrix types (the Eight Neighbors Euclidean Decimal Coding matrix, and Full Direction Matrixes). After that, we extracted Gray-Level Co-occurrence Matrix (GLCM) from those matrixes to derive the important features from each GLCM matrixes and finishing with merging the output of previous steps with Local Neighbor Patterns (LNP) histogram. For decreasing the feature vector length, we proposed five extension methods from FDLNP by choosing the specific direction matrixes. Our results demonstrate the effectiveness of our proposed algorithm on color and texture databases, comparing with recent works, with regard to the Precision, Recall, mean Average Precision (mAP), and Average Retrieval Rate (ARR). For enhancing the image retrieval accuracy, we proposed a novel framework that combined the image retrieval system with clustering and classification algorithms. Moreover, we proposed a distributed model that used our FDLNP method with Hadoop to get the ability to process a huge number of images in a reasonable time.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A First Experiment: Image Retrieval Using Fdlnpmentioning

confidence: 99%

Section: Third Experiment: Image Retrieval Using Machine Learning Classificationmentioning

confidence: 99%

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Full Direction Local Neighbors Pattern (FDLNP)

Alrahhal¹,

K.P²

2021

IJACSA

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“…Fingerprint will consider for the verification of identity which is used in colleges, companies and banking sector. The remote sensing is used for transferring, processing and retrieving the huge amount of data [4].…”

Section: Introductionmentioning

confidence: 99%

Content based Image Retrieval using Median Binary Pattern with Structure Element Descriptor on Noisy Images

Desai¹

2019

IJRASET

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Effective features are essential issue for the representation and recognition of images. In this paper, we present a new feature representation method for image retrieval based on Median Binary Pattern (MBP) and Structure Element Descriptor, named Median Binary Pattern extended structure element descriptor (MBP-SED). Firstly, the gray scale image is converted using the MBP. The elements in the structure can effectively represent the bitmap which is generated by MBP. Global Feature is achieved using the combination of MBP-SED and color Histogram (CH). Proposed system is more reliable on noisy images. Experiments on Corel-1k dataset show that MBP-SED outperforms than other descriptors.

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Content-Based Image Retrieval Techniques and Their Applications in Medical Science

Kapadia

Paunwala²

2022

EAI/Springer Innovations in Communication and Computing

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Content-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques

Cited by 20 publications

References 22 publications

Full Direction Local Neighbors Pattern (FDLNP)

Full Direction Local Neighbors Pattern (FDLNP)

Content based Image Retrieval using Median Binary Pattern with Structure Element Descriptor on Noisy Images

Content-Based Image Retrieval Techniques and Their Applications in Medical Science

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