title: Stochastic optimized relevance feedback particle swarm optimization for content based image retrieval.
abstract: One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.
authors.affiliation: Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, Malaysia.
authors.forename: Muhammad
authors.identifier: 0000-0003-1860-0736
authors.initials: M
authors.lastname: Imran
authors.affiliation: Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, Malaysia.
authors.forename: Rathiah
authors.initials: R
authors.lastname: Hashim
authors.affiliation: Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor Darul Ehsan, Malaysia.
authors.forename: Abd Khalid
authors.initials: AK
authors.lastname: Noor Elaiza
authors.affiliation: University of Engineering and Technology, Taxila, Pakistan.
authors.forename: Aun
authors.initials: A
authors.lastname: Irtaza
mesh_terms: D000465:Algorithms; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes; D060388:Support Vector Machine