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JCR Impact Factor: 0.700
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Issues per year: 4
Current issue: Aug 2024
Next issue: Nov 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  2/2020 - 12

Generation of Visual Patterns from BoVW for Image Retrieval using modified Similarity Score Fusion

ARULMOZHI, P. See more information about ARULMOZHI, P. on SCOPUS See more information about ARULMOZHI, P. on IEEExplore See more information about ARULMOZHI, P. on Web of Science, ABIRAMI, M. See more information about ABIRAMI, M. on SCOPUS See more information about ABIRAMI, M. on SCOPUS See more information about ABIRAMI, M. on Web of Science
 
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Download PDF pdficon (1,519 KB) | Citation | Downloads: 966 | Views: 2,093

Author keywords
feature extraction, image fusion, image matching, image representation, supervised learning

References keywords
image(36), retrieval(19), visual(14), vision(13), recognition(12), pattern(11), cvpr(11), words(9), fusion(8), classification(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 101 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02012
Web of Science Accession Number: 000537943500012
SCOPUS ID: 85087452294

Abstract
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The Bag of Visual Words (BoVW) turns up to be an efficient method to represent images for Content Based Image Retrieval (CBIR). Despite their significant usage, the traditional BoVW method has low discriminative power and fails to provide spatial information, which increases the false positive images and reduces the precision values. To address the first issue, a novel way of identifying a set of visual words unique for each category, named as Visual Patterns (VP) is proposed. Also, the weight for the respective VPs and a new way of score calculations for similarity matching with the database images are proposed. Then, to address the second issue of enhancing the spatial information, late fusion of Gabor filter features along with VP is proposed. As a consequence, VP provides better discriminative power and Gabor filtering, taking advantage of its complementary clue, provides spatial information. Hence, it helps to reduce the false matches and improves the precision values. Experiments are carried out on the popular datasets, namely, Caltech 256, Oxford 5K and Inria Holidays datasets along with Flickr 1M dataset. The proposed method is compared with other BoVW based models and proved that the MAP value is improved 0.50 times from the basic BoVW model.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

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Cited-By CrossRef

[1] A Novel Visual Indoor Positioning Method With Efficient Image Deblurring, Jia, Shuang, Ma, Lin, Yang, Songxiang, Qin, Danyang, IEEE Transactions on Mobile Computing, ISSN 1536-1233, Issue 7, Volume 22, 2023.
Digital Object Identifier: 10.1109/TMC.2022.3143502
[CrossRef]

[2] A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features, Chu, Tianyou, Chen, Yumin, Huang, Liheng, Xu, Zhiqiang, Tan, Huangyuan, Remote Sensing, ISSN 2072-4292, Issue 23, Volume 12, 2020.
Digital Object Identifier: 10.3390/rs12233978
[CrossRef]

[3] Encoding hieroglyph segments to represent hieroglyphs following the bag of visual word model for retrieval, Pinilla-Buitrago, Laura Alejandra, Martínez-Trinidad, José Fco., Carrasco-Ochoa, Jesús Ariel, Expert Systems with Applications, ISSN 0957-4174, Issue , 2022.
Digital Object Identifier: 10.1016/j.eswa.2022.116983
[CrossRef]

Updated 2 days ago

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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