Content based Image Retrieval based on Different Global and Local Color Histogram Methods: A Survey

Suhasini, Pallikonda Sarah; Krishna, K. Sri Rama; Krishna, Iyyanki V. Murali

doi:10.1007/s40031-016-0223-y

Cited by 15 publications

(3 citation statements)

References 28 publications

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“…The most commonly used color models are RGB, HSV, and YUV. The color feature can be described by color histogram [19], color correlogram, and a color moment [20]. In this paper, the color moment is used to represent color features from the V channel of the YUV model.…”

Section: Color Featuresmentioning

confidence: 99%

Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning

2023

Scholar J. Sci. & Tech

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Detecting and classifying diseases in agricultural crops is crucial for ensuring crop health and maximizing yields. In the case of walnut trees, timely identification of fungal diseases is essential to prevent their spread and minimize economic losses. Traditional methods of disease diagnosis rely on visual inspection by experts, which can be time-consuming and prone to human errors. In this study, we propose an intelligent system for the detection and classification of walnut fungi diseases using machine-learning techniques. The system leverages the power of image processing and pattern recognition algorithms to automate the identification process, enabling rapid and accurate diagnosis. The system is trained using a dataset comprising high-resolution images of healthy walnut leaves and leaves affected by various fungal diseases. We extract relevant features from the images and employ state-of-the-art machine learning algorithms, such as back-propagation neural network (BPNN), to learn the complex patterns associated with each disease. To evaluate the performance of our system, we conducted extensive experiments on a real-world dataset, achieving high accuracy rates for disease detection and classification. The system successfully identifies common walnut fungi diseases, including walnut blight, anthracnose, and powdery mildew, with accuracy rates exceeding 90%. Moreover, it exhibits robustness against variations in lighting conditions and leaf orientations. Our results demonstrate the potential of machine learning techniques in revolutionizing disease diagnosis in the agricultural domain. The proposed system offers a cost-effective and scalable solution for farmers and agronomists to monitor the health of walnut trees, detect diseases at an early stage, and apply targeted treatments. By providing accurate and timely diagnoses, this technology can contribute to reducing crop losses and improving overall productivity in walnut cultivation.

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Section: Color Featuresmentioning

confidence: 99%

Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning

2023

Scholar J. Sci. & Tech

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“…c Examples of ornaments of the same type in our dataset. It may be observed that such similar ornaments demand much finer similarity measurements for similar product retrieval It has also been reported that a region/block-based histogram similarity can be useful in many image matching and retrieval tasks [51].…”

Section: Related Workmentioning

confidence: 99%

Ornament Image Retrieval Using Multimodal Fusion

Sekh¹,

Joardar

Dogra

2021

SN COMPUT. SCI.

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Search-by-example, i.e. finding images that are similar to a query image, is an indispensable function for various modern image search engines. The applications of such systems are manifold. The primary application of search-by-example is in recommending fashion materials based on user interests. There are various challenges in this area of research such as a large volume of the product database, similar visual appearances, and a large variety of products. The problem becomes more difficult to solve when the product is complex in design such as ornaments. In this paper, we have proposed a fusion-based retrieval model. The method uses weighted average of multiple similarity measures. We have used four different methods namely hash-based, histogram-based, deep feature comparison, and feature cross correlation to find the similarity. A dataset of ornaments (golden earrings) has been prepared and made available to the research community. We achieve 81% top-1 and 89% top-5 accuracy using the proposed method. The dataset and the code is available publicly in https:// github. com/ skari fahmed/ RingF IR.

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“…According to literature surveys up to date, there are many approaches to be used for image retrieval or similarity, using global and local histograms, or simply choosing one of them. However, the use of local histograms is not found in comparing images of different dimensions (Cha, 2007;Suhasini et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Farkli Boyutlarda Görüntülerde Uyarlamali Yerel Pencere İle Benzerli̇k Ölçümü

Kılıçaslan

Tanyeri

İncetaş

et al. 2017

Mühendislik Bilimleri Ve Tasarım Dergisi

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İçerik tabanlı görüntü erişim yöntemleri, renk, desen ve şekil bilgileri gibi farklı özelliklere ihtiyaç duymaktadır. Araştırmacılar, görüntü histogramından elde edilen verileri de bu bağlamda kullanmaktadır. Histogram bilgileri, yerel veya global olarak hesaplanır. Ancak, aynı içeriğe sahip olsalar da, farklı en / boy oranlarına sahip görüntülerde yerel yaklaşımlar kullanılamamakta ve tüm pikselleri işleyen yöntemler ile de her zaman istenilen sonuca varılamamaktadır. Bu çalışmada, farklı boyutlarda iki görüntüden, eşit sayıda pencere alınarak, görüntülerin benzerlik ölçümünde kullanılan ve yerel histograma dayanan yeni bir yöntem geliştirilmiştir. Geliştirilen yöntem, Weizmann tekli nesne görüntü bölütleme veritabanındaki 100 görüntü üzerinde test edilmiş ve yöntemin başarısı global histogram yaklaşımlarıyla karşılaştırılmıştır.

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Content based Image Retrieval based on Different Global and Local Color Histogram Methods: A Survey

Cited by 15 publications

References 28 publications

Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning

Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning

Ornament Image Retrieval Using Multimodal Fusion

Farkli Boyutlarda Görüntülerde Uyarlamali Yerel Pencere İle Benzerli̇k Ölçümü

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