Image indexing using color histogram and k-means clustering for optimization CBIR in image database

Rejito, Juli; Abdullahi, Atje Setiawan; Akmal, Akmal; Setiana, Deni; Ruchjana, Budi Nurani

doi:10.1088/1742-6596/893/1/012055

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…training pictures should be chosen for every of the categories, and once every element within the image (including those used as training set) are evaluated, and assigned to a land cover category, the accuracy of the classification needs to be assessed. [10] Unsupervised Classification: Categorization of digital image information by computer process based mostly only on the image statistics while not accessibility of training samples or a-priori data of the area. Category is not known and structure is to be discovered automatically.…”

Section: Local Binary Patternmentioning

confidence: 99%

“…[10] Classification which has training and testing processes, wherever features extracted from training pictures are compared with those extracted from testing pictures. The image is then classified based on the matched features.…”

mentioning

confidence: 99%

“…Image acquisition, segmentation and have extraction comes below the training method. An easy training and testing method is shown in figure4 [10].…”

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confidence: 99%

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Content Based Image Retrival Using Different Clustering Techniques

2023

IJSREM

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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user applications. Content-based Image retrieval deals with the extraction of knowledge, image data relationship, or other patternsnot expressly keep within the pictures. It uses ways from computer vision, image processing, image retrieval, data retrieval, machine learning, database and artificial intelligence. Rule retrieval has been applied to large image databases. The proposedsystem gives average accuracy of 90%. Keywords— CBIR, Color feature, Shape feature, Texture feature, Feature extraction, Clustering, Image Retrieval.

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Section: Local Binary Patternmentioning

confidence: 99%

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confidence: 99%

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Content Based Image Retrival Using Different Clustering Techniques

2023

IJSREM

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“…Simple and efficient: K-means clustering algorithm is a simple and efficient clustering algorithm, which is suitable for metal abrasive image segmentation tasks. Its principle is simple, the calculation speed is fast, and it can quickly process large-scale image data [14] 2. No prior knowledge required: The K-means clustering algorithm is an unsupervised learning method that does not require prior knowledge or labeled data.…”

Section: Algorithm Superioritymentioning

confidence: 99%

Metal Abrasive Image Segmentation Algorithm Based on K-means Clustering

Zhang,

Wu,

et al. 2023

AJST

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Metal abrasive image segmentation is one of the important image processing tasks in the industrial field. However, due to the complex color and texture characteristics of metal abrasive images, as well as difficult factors such as noise and lighting changes, traditional image segmentation methods often fail to achieve high accuracy and stability. In order to solve this problem, a metal abrasive image segmentation algorithm based on K-means clustering is proposed. The algorithm applies the K-means clustering algorithm to the image segmentation of metal abrasive particles, and realizes the separation of metal abrasive grains from the background by clustering the color features of image pixels. Experimental results show that our algorithm shows good accuracy and stability in the metal abrasive image segmentation task, and has high efficiency. Therefore, the algorithm provides an accurate and efficient method for metal abrasive image segmentation.

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“…2: An example of the incremental frame for image portioning ii) K-Means Clustering: To obtain a cluster of regions, k-mean clustering was used. It is because is one of the most widely used clusterings and is the ease of implementation, simplicity, efficiency, and empirical success (Mamat et al, 2015;Zambre and Patil, 2013;Rejito et al, 2017). Fig.…”

Section: Step 2: Forming the Regionmentioning

confidence: 99%

Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

al.¹

2022

Int. j. adv. appl. sci.

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Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).

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Image indexing using color histogram and k-means clustering for optimization CBIR in image database

Cited by 12 publications

References 2 publications

Content Based Image Retrival Using Different Clustering Techniques

Content Based Image Retrival Using Different Clustering Techniques

Metal Abrasive Image Segmentation Algorithm Based on K-means Clustering

Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

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