Design and implementation of CBIR system for academic/educational purposes

Marinov, Miroslav; Valova, Irena; Kalmukov, Yordan

doi:10.1109/icai50593.2020.9311353

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The experimental CBIR system used is described in details in [9] and [10]. Briefly, the formation of a feature vector for each image is a sequence of the following actions:…”

Section: Experimental Environment and Evaluationmentioning

confidence: 99%

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Experimental Evaluation of Basic Similarity Measures and their Application in Visual Information Retrieval

Marinov¹,

Kalmukov²,

Valova³

2022

IJACSA

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Searching for similar images is an important feature for image databases and decision support systems in various subject domains. However, it is essential that search results are sorted by degree of similarity in reverse order. This paper presents a comparative analysis of four existing similarity measures and experimentally tests whether they could be used to calculate similarity between images. Metrics could be evaluated by comparing their results to the cumulative human perception of similarity between the same images, obtained by real people. However, this introduces a lot of subjectivism due to nonuniform judgement and evaluation scales. The paper presents a more objective approach -checks which measure performs best in retrieving more images, containing objects of the same type. Results show all four measures could be used to calculate similarity between images, but Jaccard's index performs best in most cases, because it compares features vectors positionally and thus indirectly consider shape, position, orientation and other features.

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“…The experimental CBIR system used is described in details in [9] and [10]. Briefly, the formation of a feature vector for each image is a sequence of the following actions:…”

Section: Experimental Environment and Evaluationmentioning

confidence: 99%

“…• All pixels in all the blocks are converted from RGB to one of our 64 primary colors. How these 64 colors were selected and the process of color transformation is described in our previous research [9], [10].…”

Section: Experimental Environment and Evaluationmentioning

confidence: 99%

Experimental Evaluation of Basic Similarity Measures and their Application in Visual Information Retrieval

Marinov¹,

Kalmukov²,

Valova³

2022

IJACSA

Self Cite

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“…Academic/Educational Design and implementation of CBIR system for academic/ educational purposes [5].…”

Section: Covid-19mentioning

confidence: 99%

“…With the increasing usage of internet and digital gadgets, content-based image retrieval (CBIR) has grown and been applied in fields such as artificial vision and artificial intelligence [1]. Currently, improvements have been reported in new CBIR approaches and several effective algorithms have been established that allow searching and retrieving images (by content) from an input image [2][3][4][5][6]. The application areas include: fashion, people identification, e-commerce recovery products, remote sensing recovery images, brand images recovery, natural scenes recovery, among others [7,8].…”

Section: Introductionmentioning

confidence: 99%

Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

et al. 2021

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One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. The current focus of the researchers is to develop methods for the content based exploration of natural scenery images. In this research paper, a self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. Once the causal relationships are obtained, the k-means algorithm is applied to achieve the self-organizing of these attributes. Regarding classification, the k−NN distance classification algorithm is used to find the most similar images that share the causal relationships between the elements of the scenes. The proposed methodology is validated on three public image databases, obtaining 100% recovery results.

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Design and implementation of experimental training board multi-medical sensors for educational purposes

Alsabah,

Aljarah,

Jameel

2023

The Fourth Scientific Conference for Electrical Engineering Techniques Research (Eetr2022)

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Design and implementation of CBIR system for academic/educational purposes

Cited by 5 publications

References 5 publications

Experimental Evaluation of Basic Similarity Measures and their Application in Visual Information Retrieval

Experimental Evaluation of Basic Similarity Measures and their Application in Visual Information Retrieval

Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

Design and implementation of experimental training board multi-medical sensors for educational purposes

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