A Comparative Analysis for CBIR Using Fast Discrete Curvelet Transform

Sugamya, Katta; Pabboju, Suresh; Babu, A. Vinaya

doi:10.1007/978-981-10-3818-1_3

Cited by 2 publications

(2 citation statements)

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“…Wedge wrapping is an application of the "wrapping based Curvelet Transform" [15]. FDCTW has faster computation time than the USFFT technique [18].The reason of fast computation of this technique is consider to two reasons,first, it doesn't use the ridgelet transform so, it reduces the amount of redundancy in transform and makes the speed higher [19]. Second, the low computation time is also become of using a series of translation and wraparound technique [20].…”

Section: B Fast Discrete Curvelet Transform Via Wrapping (Fdctw)mentioning

confidence: 99%

Review in Images Enhancement Medical Using Curvelet Transform and Vector Information

Almola

2022

IJICI

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The use of medical imaging for the purposes of medical monitoring and diagnosis has seen tremendous growth in recent years, which has resulted in a rising need for suitable data storage and accessibility. The primary goal of image enhancement is to generate a processed image that is suitable for a specific application. This can be accomplished by acquiring the image in the highest resolution possible or by using a variety of techniques to extract the important information while ignoring the irrelevant information. Image processing can then be performed on the processed image. Image enhancement techniques are used in medical imaging to increase the quality of the visual representation of an image by reducing the amount of noise present and sharpening the features. This study examines the Curvelet transform-based approach for increasing medical image quality. The contribution of the proposed review two steps are advised for optimizing the images. The first step is the optimization step utilizing the Curvelet transform, which featured a series of image-preparation processors. Curvelet's ability to extract complete spectrum information from an image with varying edge orientations makes it valuable for assessing data about wave propagation. In light of this, the curvelet transform is considered an excellent filter for improving and denoising images, and it is intended to deal with components in several orientations and scales. The primary objective of this treatment is to boost visibility and eliminate noise, which is superior to conventional techniques of enhancement in which the enhancement step is performed by discrete curvelet transform through wrapping. The contribution of the proposed review the usual vector is the second step of this review approach. The vector image is distributed randomly to each pixel in images that are unclear and noisy. This procedure eliminates the unpredictability in the previously computed directions and makes them regular, producing more precise results and enhancing image clarity, particularly at the image's edges. By comparing the results of the proposed technique of improvement to the results of existing improvement methods, it was determined that the proposed approach yields superior performance and is more efficient. The efficiency and efficacy of the suggested technique may be determined based on the results of the trials, which demonstrated that the method performs very well and yields accurate and precise results.

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Section: B Fast Discrete Curvelet Transform Via Wrapping (Fdctw)mentioning

confidence: 99%

Review in Images Enhancement Medical Using Curvelet Transform and Vector Information

Almola

2022

IJICI

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“…Sugamya et al 8 proposed CBIR where two techniques are used for a comparative analysis. The types of techniques decide the overall performance of comparative analysis used in extracting multiple features and the measures of similarity among query image and image database.…”

Section: Introductionmentioning

confidence: 99%

A dynamic wrapper‐based feature selection for improved precision in content‐based image retrieval

Sankar

Maheswari

2019

Concurrency and Computation

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The content-based image retrieval (CBIR) has been applied in the image processing as well as pattern recognition. A challenging task in the CBIR research has been through the feature extraction for decreasing any semantic gap that is an active research topic. Here, in this work, there is a texture feature that is extracted from an image, making use of a technique of curvelet transform. This curvelet is selected for a sparse representation that is quite critical for the estimation of images, which have been de-noised with some inversed problems. The wrapping-based curvelet transform will be even more robust as well as faster in the time of computation than the Ridge Transform. A technique of feature selection will be brought for selecting the optimal features. The correlation-based feature selection (CFS) method has been adapted for improving the accuracy of the CBIR systems. The non-deterministic polynomial (NP)-hard problems may be solved using the chemical reaction optimization (CRO) that has been motivated using the technique of chemical reaction. The benefits of proposed method is a necessity for marginal human intrusion for the purpose of retrieving the images needed from its database. The proposed mechanism has been again assessed depending on the Coral Database, and a performance study is ended using precision, f measure, and recall.

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A Comparative Analysis for CBIR Using Fast Discrete Curvelet Transform

Cited by 2 publications

References 5 publications

Review in Images Enhancement Medical Using Curvelet Transform and Vector Information

Review in Images Enhancement Medical Using Curvelet Transform and Vector Information

A dynamic wrapper‐based feature selection for improved precision in content‐based image retrieval

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