Image Retrieval based on the combination of Color Histogram and Color Moment

Singh, Sandeep; Hemachandran, K.

doi:10.5120/9263-3441

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Since any visual feature can benefit from this approach and it not restricted to any feature, from the 1300 images, we extract the color histogram [ 30 ] and the edge histogram features [ 31 ] as examples. After concatenating the two features, entries between 1 and 150 correspond to the edge histogram, and entries between 151 and 341 correspond to the color histogram.…”

Section: Methodsmentioning

confidence: 99%

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

2020

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We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.

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Section: Methodsmentioning

confidence: 99%

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

2020

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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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“…This approach is straightforward and very simple and normally uses an equal weight value scheme assigned to any of the feature spaces. Several studies have followed this method, such as that in Singh and Hemachandran (2012) and Alsmadi (2020). Feature weighting for early fusion is suitable when the retrieval process is considered as a classification task in which pools of images are assigned to a set of labels.…”

Section: Related Workmentioning

confidence: 99%

Implementation of early and late fusion methods for content-based image retrieval

Ahmed¹,

Mohamed²

2021

Int. j. adv. appl. sci.

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Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

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Image Retrieval based on the combination of Color Histogram and Color Moment

Cited by 12 publications

References 22 publications

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning

Implementation of early and late fusion methods for content-based image retrieval

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