Content-based image retrieval is a technique for locating images in vast, unlabeled image collections (CBIR). However, users are not happy with the traditional methods of information retrieval. Additionally, the number of consumer-accessible pictures and online production and distribution channels are expanding. Consequently, permanent and widespread digital image processing occurs across numerous industries. As a result, acquiring quick access to these big image databases and extracting identical images from sizable groups of photographs from a specific image (Query) create significant problems that call for efficient solutions. Calculations related to similarity and feature representation are crucial to a CBIR system's effectiveness. Color, shape, texture, and gradient are some essential features that can be utilized to portray an image. Local Binary Pattern (LBP) is a modest and successful texture controller that marks the pixels of an image by controlling the part of every pixel and deciphering the outcome as a binary value. The Local Binary Pattern (LBP) approach is acquainted with grey-level images to characterize color images as the pattern's dimensionality is enhanced. The current study proposes the 'Median Binary Pattern', which incorporates the multichannel decoded Local Binary Pattern (mdLBP) utilized to portray color images. For consolidating LBPs from more than one channel to make the descriptor noise-robust, two structures, specifically adder and decoder-based structures, and a noise-robust binary pattern called the 'Median Binary Pattern'. Compared with existing approaches, the proposed method achieved Average Recovery precision (ARP) and Average Recovery rate (ARR) of 68.1 and 33.55, respectively, with Noise Robust Binary Patterns.
The main objective of medical imaging is to get a extremely informative image for higher designation. One modality of medical image cannot offer correct and complete data in several cases. In brain medical imaging, resonance Imaging (MRI) image shows structural data of the brain with none useful information, wherever as pc imaging (CT) image describes useful data of the brain however with low spatial resolution particularly with low dose CT scan, that is helpful to scale back the radiation impact to physique. Within the field of diagnosing, Image fusion plays a really very important role. Fusing the CT and tomography pictures provides a whole data concerning each soft and exhausting tissues of the physique. This paper proposes a 2 stage hybrid fusion formula. Initial stage deals with the sweetening of a coffee dose CT scan image exploitation totally different image sweetening techniques viz., bar graph Equalization and adaptation bar graph deed. Within the second stage, the improved low dose CT scan image is united with tomography image exploitation totally different fusion algorithms viz., distinct rippling rework (DWT) and Principal element Analysis (PCA). The projected formula has been evaluated and compared exploitation totally different quality metrics.
Rice is a widely cultivated grain with numerous genetic variants that can be distinguished by their unique texture, shape, and color characteristics. Accurate classification and evaluation of seed quality depend on the ability to identify these traits. In this study, we propose a novel Hybrid Enhanced Featured AlexNet model for identifying eight varieties of milled rice, including arborio, basmati, ipsala, jasmine, jhili, masoori, HMT, and karacadag. Our approach combines the use of a pre-trained AlexNet model with multilayer feature fusion to extract deep features, which are then supplied to a Support Vector Machine (SVM) for classification. Our proposed model achieves an impressive accuracy of 99.63%, sensitivity of 99.63%, specificity of 99.95%, precision of 99.64%, and an F1 score of 99.63%. Our methodology has significant potential for application in the food processing sector to determine the price of various milled rice varieties.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.