Architectural Formation with Deep Learning and Algorithmic Bindings for Cross-Domain Information Retrieval

Clicks: 1
ID: 312908
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #381 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Efficient strategies for index search are crucial elements involved in categorizing and retrieving simple as well as complex image collections and libraries. In this paper, new algorithm is presented aimed at refining the selection of images to be clustered and more accurate identification of ROIs in many clustered objects.  The relations to other features are also expected to be provided, including the RGB image features and the other feature sets obtained with the use of Convolutional Neural Networks (CNNs) for achieving the scale invariance. Despite, GoogleNet and AlexNet and ResNet exist this algorithm has the deep feature and spatial data point of view for improving the image classification. Feature coefficient computation further enables the application of norms L1 and L2 on over the images of RGB. The ‘Scale invariance’ encompasses predicting the scaling of keypoints, computation of coefficients between two successive octaves along with expressions of virtual intra octave expressions. In the process of maxima selection, interpolation, non-maxima suppression, and cumulative thresholding the algorithm applies ROI detection. The presented multimodal approach significantly enhances the identification of objects particularly in a setting as depicted in this paper with high density of other similar objects. The color feature sets and CNN feature sets that are integrated in constructing the Bag-of-Words (BoW) model improve image indexation and image search. From the quantitative analysis, there is promising average precision (AP) and average recall (AR) when the presented algorithm is tested using data from Corel-10K, Tropical Fruits and Cifar-10 datasets.
Reference Key
imported_1777057746_69ebbfd22aba1 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aleema Sadia
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/497
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.