Stochastic optimized relevance feedback particle swarm optimization for content based image retrieval

• Main Authors: Hashim, R. (Author), Imran, M. (Author), Irtaza, A. (Author), Noor Elaiza, A.K (Author)
• Format: Article
• Language: English
• Published: Hindawi Publishing Corporation 2014
• Subjects: algorithm; Algorithms; article; automated pattern recognition; classification algorithm; computer assisted diagnosis; content based image retrieval; data base; feedback system; Fourier transformation; genetic algorithm; human computer interaction; Image Interpretation, Computer-Assisted; image retrieval; information retrieval; Information Storage and Retrieval; mathematical parameters; Models, Theoretical; negative feedback; particle swarm optimization support vector machine relevance feedback; Pattern Recognition, Automated; positive feedback; procedures; semantics; statistics; stochastic model; Stochastic Processes; support vector machine; Support Vector Machines; theoretical model
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 23566140 (ISSN)
• DOI: 10.1155/2014/752090

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. © 2014 Muhammad Imran et al.