Image Retrieval Method based on Integration of Principal Component Analysis and Multiple Features
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@Article{JICS-14-195,
author = {Jingji Zhao},
title = {Image Retrieval Method based on Integration of Principal Component Analysis and Multiple Features},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {14},
number = {3},
pages = {195--202},
abstract = {Jingji Zhao
School of Mathematics and Statistics, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
(Received May 11 2019, accepted July 20 2019)
Existing content-based image retrieval methods exist some drawbacks, such as low retrieval
precision, unstable performance. To address these drawbacks, in this paper a content-based image retrieval
method is presented based on multi-feature fusion of principal component, oriented-gradient and color
histogram. The idea for the proposed method is: firstly, input image is grayscale and flattened into a one-
dimensional vector, and the first n principal components from the vector yielded by the PCA algorithm are
extracted, in other word, input image is represented as a n×1 dimensional PCA feature vector. Secondly, to
remedy color and orientation information missed by PCA, oriented-gradient and color histograms are used to
extract orientation and color features respectively. Thirdly, extracted oriented-gradient and color histograms
are merged with PCA features to generate the multi-feature representation of the input image. This paper
confirms that the proposed multi-feature method can better represent an input image and can easily measure
the similarity between images. The experiments are carried out and evaluated based on Corel-1000 , the target
method is significantly better than the four popular methods.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22413.html}
}
TY - JOUR
T1 - Image Retrieval Method based on Integration of Principal Component Analysis and Multiple Features
AU - Jingji Zhao
JO - Journal of Information and Computing Science
VL - 3
SP - 195
EP - 202
PY - 2024
DA - 2024/01
SN - 14
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22413.html
KW - contented-based image retrieval(CBIR)
KW - principal component analysis(PCA)
KW - histogram of
oriented gradient(HOG)
KW - histogram of color(HoC)
KW - similarity
AB - Jingji Zhao
School of Mathematics and Statistics, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
(Received May 11 2019, accepted July 20 2019)
Existing content-based image retrieval methods exist some drawbacks, such as low retrieval
precision, unstable performance. To address these drawbacks, in this paper a content-based image retrieval
method is presented based on multi-feature fusion of principal component, oriented-gradient and color
histogram. The idea for the proposed method is: firstly, input image is grayscale and flattened into a one-
dimensional vector, and the first n principal components from the vector yielded by the PCA algorithm are
extracted, in other word, input image is represented as a n×1 dimensional PCA feature vector. Secondly, to
remedy color and orientation information missed by PCA, oriented-gradient and color histograms are used to
extract orientation and color features respectively. Thirdly, extracted oriented-gradient and color histograms
are merged with PCA features to generate the multi-feature representation of the input image. This paper
confirms that the proposed multi-feature method can better represent an input image and can easily measure
the similarity between images. The experiments are carried out and evaluated based on Corel-1000 , the target
method is significantly better than the four popular methods.
Jingji Zhao. (2024). Image Retrieval Method based on Integration of Principal Component Analysis and Multiple Features.
Journal of Information and Computing Science. 14 (3).
195-202.
doi:
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