An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization

Image retrieval system as a reliable tool can help people in reaching efficient use of digital image accumulation; also finding efficient methods for the retrieval of images is important. Color and texture descriptors are two basic features in image retrieval. In this paper, an approach is employed which represents a composition of color moments and texture features to extract low-level feature of an image. By assigning equal weights for different types of features, we can’t obtain good results, but by applying different weights to each feature, this problem is solved. In this work, the weights are improved using a modified Particle Swarm Optimization (PSO) method for increasing average Precision of system. In fact, a novel method based on an evolutionary approach is presented and the motivation of this work is to enhance Precision of the retrieval system with an improved PSO algorithm. The average Precision of presented method using equally weighted features and optimal weighted features is 49.85% and 54.16%, respectively. 4.31% increase in the average Precision achieved by proposed technique can achieve higher recognition accuracy, and the search result is better after using PSO.

@article{Heidari2016,
  title     = {An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {7},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Hadis Heidari and Abdolah Chalechale},
  doi       = {10.14569/IJACSA.2016.070715},
  url       = {https://doi.org/10.14569/IJACSA.2016.070715}
}