Colour Features Extraction Techniques and Approaches for Content-Based Image Retrieval (CBIR) System

Abdullah, Muhammad Naim; Shukran, Mohd Afizi Mohd; Isa, Mohd Rizal Mohd; Ahmad, Nor Suraya Mariam; Khairuddin, Mohammad Adib; Yunus, Mohd Sidek Fadhil Mohd; Ahmad, Faudziah

doi:10.4236/msce.2021.97003

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…It also happens in an image. Colour is the most important element as it carries important information of the whole image [9]. However, in any case, colour is a perplexing point and hard to comprehend.…”

Section: Related Work On Fresh Fruit Bunches Ripeness Classificationmentioning

confidence: 99%

Short Review on Palm Oil Fresh Fruit Bunches Ripeness and Classification Technique

Ghazalli

Selamat

Khamis

et al. 2023

ARAM

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High quality palm oil is critical in ensuring Malaysia’s competitiveness in the sector. Studies have shown that there is a significant relationship between the quality of palm oil produced and the ripeness of the fruits used in producing the oil. Correct ripeness of the fresh fruit bunches (FFB) produces higher quality and more oil content. Unripe FFB produces the least oil and overripe FFB produces oil of lower quality. According to Malaysian Palm Oil Board (MPOB), the main factors that determine the ripeness of the oil palm FFB are its colour and the number of its loose fruits. To classify the ripeness according to these two factors, there are 3 common techniques has been implemented in the previous work which are; colour feature extraction, texture feature extraction and Deep Learning method. To handle fruit ripeness classification problem, this paper provides a short review to the reader to grasp the applicable technique that can be implemented.

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Section: Related Work On Fresh Fruit Bunches Ripeness Classificationmentioning

confidence: 99%

Short Review on Palm Oil Fresh Fruit Bunches Ripeness and Classification Technique

Ghazalli

Selamat

Khamis

et al. 2023

ARAM

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“…The extraction feature used in this study is the color feature [11]. Therefore, in this study, color became very important in determining the degree of ripeness of tomatoes [12], [13].…”

Section: Color Feature Extractionmentioning

confidence: 99%

Analysis of Tomato Ripeness by Color and Texture Using Cielab and K-Means Clustering

Denih,

Negara,

Marzuki

2023

JIIKM

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Humans have limitations, including in the identification of tomatoes. With the nature of limitations, it makes it difficult for humans to identify the ripeness of tomatoes in large quan- tities. So far, the selection and determination of the quality activity of tomatoes is carried out manually, resulting in a less uniform product. Manual identification of tomato ripeness has many disadvantages caused by many factors, such as fatigue, lack of motivation, experience, proficiency and so on. This study aims to create a tomato maturity level analysis system based on color and texture using CIELAB and K-Means clustering as a method to determine tomato maturity precisely and accurately. This system displays five images, namely RGB, CIELAB, K-Means clustering, binary and grayscale images, after entering the tomato image, the image will be processed using the five images and the results of extracting characteristics from the tomato will come out. The accuracy rate of tomato ripeness has an average value of 92.70%. The benefit of this research is that it can save time in classifying tomato ripeness and make it easier to determine tomato ripeness based on color.

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“…CBIR systems work by extracting visual features from images, such as color, texture, and shape. These features are then used to compare images and find the best matches [2]. This is in contrast to traditional methods of image retrieval, which rely on textual metadata such as tags and keywords.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

Battur,

Narayana

2023

IJACSA

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This paper proposes an innovative ensemble learning framework for classifying medical images using Support Vector Machine (SVM) and Fuzzy Logic classifiers. The proposed approach utilizes logical AND and OR operations to combine the predictions from the two classifiers, aiming to capitalize on the strengths of each. The SVM and Fuzzy Logic classifiers were independently trained on a comprehensive database of medical images comprising various types of X-ray images. The logical OR operation was then used to create an ensemble classifier that outputs a positive classification if either of the individual classifiers does so. On the other hand, the logical AND operation was used to construct an ensemble classifier that outputs a positive classification only if both individual classifiers do so. The proposed method aims to increase sensitivity and precision by capturing as many positive instances as possible, thereby reducing false positives. The scope of the proposed work is validated in terms of overall time complexity and retrieval accuracy. The simulation outcome shows promising result with 98.36 accuracy score and 1.8 seconds to retrieve all the images in query database.

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Colour Features Extraction Techniques and Approaches for Content-Based Image Retrieval (CBIR) System

Cited by 6 publications

References 11 publications

Short Review on Palm Oil Fresh Fruit Bunches Ripeness and Classification Technique

Short Review on Palm Oil Fresh Fruit Bunches Ripeness and Classification Technique

Analysis of Tomato Ripeness by Color and Texture Using Cielab and K-Means Clustering

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

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