2022
DOI: 10.1007/978-981-16-5640-8_5
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Exploration on Content-Based Image Retrieval Methods

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Cited by 5 publications
(5 citation statements)
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“…The tools [17] include functions like "data grooming" [27], which denotes transforming raw data into analyzable data with various data structures. Other approaches [24] focus on transforming human-readable data into machine-readable data considering inconsistencies in data formatting given that they are produced under different conditions. The idea is to exhibit processes, digital spaces, and systems that host datasets and provide them with access to understand the conditions in which data are processed.…”
Section: Data Explorationmentioning
confidence: 99%
“…The tools [17] include functions like "data grooming" [27], which denotes transforming raw data into analyzable data with various data structures. Other approaches [24] focus on transforming human-readable data into machine-readable data considering inconsistencies in data formatting given that they are produced under different conditions. The idea is to exhibit processes, digital spaces, and systems that host datasets and provide them with access to understand the conditions in which data are processed.…”
Section: Data Explorationmentioning
confidence: 99%
“…It also shows us the computational costs of building the LSA and LDA matrices [4] The steps that are needed before applying LDA to textual data include appropriate pre-processing, adequate selection of model parameters, evaluation of model's reliability, and process of validly interpreting results. This paper aims to provide more information and accessibility to researchers in LDA topic modeling and to create a hands-on technique to apply topic modeling [5][6][7][8][9][10][11][12][13].…”
Section: Literature Surveymentioning
confidence: 99%
“…An input image is the first step in a CBIR pipeline inquiry. A feature vector is generated when features are extracted from this image [11]. Only feature vectors with a proximity to the input vector are returned after the input feature vector has been compared to every feature vector in the database.…”
Section: Figure 1 Sample Images For Cbirmentioning
confidence: 99%
“…This kind of noise is known as gunshot noise, spike noise, or impulse noise [10,11], which is generally affected by wrong retention areas, failure of pixel constituents in the camera sensors, or there may be mistakes in timing during digitization. To mitigate noise, filtering methods are utilized.…”
Section: Salt and Pepper Noisementioning
confidence: 99%