TY - JOUR
T1 - A Salient Object-Based Image Retrieval Using Shape and Color Features
AU - Shuxian Huang and Wenbing Chen
JO - Journal of Information and Computing Science
VL - 3
SP - 201
EP - 211
PY - 2024
DA - 2024/01
SN - 13
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22446.html
KW - Image retrieval
KW -  salient object
KW -  region
KW -  shape
KW -  object detection
KW -  similarity measure. 

AB - Shuxian Huang, Wenbing Chen 
Nanjing University of Information Science and Technology, Nanjing 210044, China 
 (Received January 17 2018, accepted July 05 2018) 
 In  this  paper,  a  salient  object-based  image  retrieval  method  (SOBIR)  is  presented,  which  linearly 
combines the shape and colour features of the salient objects contained in target and candidate images respectively to 
carry out content-based image retrieval (CBIR). The framework of the proposed method is carried out as follows: first, 
the mean shift and region growing algorithms are used to segment an input image into many regions; secondly, based 
on these regional contrasts the saliency map, the binary image, and the salient object image are extracted respectively; 
thirdly,  the  shape  representation  of  the  salient  object  is  extracted  from  the  binary  image  using  an  improved  polar 
Fourier Descriptor method, meanwhile the salient object contained in the input image is converted into a representation 
of its histogram in the L∗a∗b∗ colour space; Finally, the similarity between the two salient objects contained in the 
target and candidate images is defined by linearly combining both the shape and colour representations. Experimental 
results  show  that,  compared  to  the  latest  two  CBIR  methods,  the  proposed  SOBIR  method  exhibits  an  excellent 
performance in precision, recall, flexibility and efficiency.