@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}
}