Title :
An Image Retrieval Method Based on r/KPSO
Author :
Zhang, Xu ; Guo, Bao-Long ; Zhang, Guiyue ; Yan, Yunyi
Author_Institution :
Sch. of Electro-Mech. Eng., Xidian Univ., Xi´´an, China
Abstract :
Image retrieval is a hot and hard technology in the field of computing science. In this paper, a method named r/KPSO (Particle Swarm Optimization with r- and K-selection) is applied in relevance feedback (RF) of image retrieval. The main idea of r/KPSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. Based on r/KPSO, we define the positive and negative feedback samples as study principle, and optimize weightings according to user´s retrieval requirement. Experiments show that both the recall and precision are improved effectively.
Keywords :
image retrieval; particle swarm optimisation; relevance feedback; K-selection; computing science; image retrieval method; negative feedback; parent care; particle swarm optimization; positive feedback; r-selection; r/KPSO; relevance feedback; user retrieval requirements; Image retrieval; Optimization; Particle swarm optimization; Productivity; Radio frequency; Space exploration; Support vector machines; image retrieval; r- and K-selection; relevance feedback;
Conference_Titel :
Innovations in Bio-inspired Computing and Applications (IBICA), 2011 Second International Conference on
Conference_Location :
Shenzhan
Print_ISBN :
978-1-4577-1219-7
DOI :
10.1109/IBICA.2011.22