TY - JOUR
T1 - How to Make a Query in Image Retrieval with Partial Information Extracted from Multiple Image Samples?
AU - Chan, Sheung Wai
AU - Cheung, Yiu Ming
N1 - Funding Information:
Project ITS/339/18, and in part by the Shenzhen Science, Technology and Innovation Committee (SZSTI) under Grant JCYJ20160531194006833.
Funding Information:
This work was supported in part by the NSFC under Grant 61672444, in part by HKBU under Grant RC-FNRA-IG/18-19/SCI/03 and Grant RC-IRCMs/18-19/ SCI/01, in part by the ITF of ITC of the Government of the Hong Kong SAR under
Publisher Copyright:
© 2021 World Scientific Publishing Company.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - The existing image retrieval methods generally require at least one complete image as a query sample. From the practical point of view, a user may not have an image sample in hand for query. Instead, partial information from multiple image samples would be available. This paper therefore attempts to deal with this problem by presenting a novel framework that allows a user to make an image query composed of several partial information extracted from multiple image samples via Boolean operations (i.e. AND, OR and NOT). Based on the request from the query, a Descriptor Cluster Label Table (DCLT) is designed to efficiently find out the result of Boolean operations on partial information. Experiments show the promising result of the proposed framework on commodity query and criminal investigation, respectively, although it is essentially applicable to different scenarios as well by changing descriptors.
AB - The existing image retrieval methods generally require at least one complete image as a query sample. From the practical point of view, a user may not have an image sample in hand for query. Instead, partial information from multiple image samples would be available. This paper therefore attempts to deal with this problem by presenting a novel framework that allows a user to make an image query composed of several partial information extracted from multiple image samples via Boolean operations (i.e. AND, OR and NOT). Based on the request from the query, a Descriptor Cluster Label Table (DCLT) is designed to efficiently find out the result of Boolean operations on partial information. Experiments show the promising result of the proposed framework on commodity query and criminal investigation, respectively, although it is essentially applicable to different scenarios as well by changing descriptors.
KW - Boolean operation
KW - descriptor cluster label table
KW - Example-based image retrieval
KW - partial descriptor information
UR - http://www.scopus.com/inward/record.url?scp=85103494131&partnerID=8YFLogxK
U2 - 10.1142/S0218001421540215
DO - 10.1142/S0218001421540215
M3 - Journal article
AN - SCOPUS:85103494131
SN - 0218-0014
VL - 35
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 7
M1 - 2154021
ER -