Relevance Feedback Based on Particle Swarm Optimization for Image Retrieval

In image retrieval, relevance feedback (RF) is an effective approach to reduce the gap between semantic concepts and low-level visual features, thus it captures user’s search intention to some extent. This paper presents two content-based image retrieval strategies with RF based on particle swarm optimization (PSO). The first strategy exploits user indication of positive images. The second one considers not only the positive but also the images indicated as negative. Both two RF strategies are improvements of query point movement by assigning positive and negative images with different weights. These weights are learned by PSO algorithm. Experiments on Corel 5000 database show the competitiveness of our algorithm.

[1]  Wu Yue-shu.  Positive relevance feedback algorithm based on dynamic weight query point movement , 2009 .

[2]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[3]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[4]  Marcello Pelillo,et al.  Content-based image retrieval with relevance feedback using random walks , 2011, Pattern Recognit..

[5]  Ricardo da Silva Torres,et al.  Content-Based Image Retrieval: Theory and Applications , 2006, RITA.

[6]  Weiguo Fan,et al.  Relevance feedback based on genetic programming for image retrieval , 2011, Pattern Recognit. Lett..

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Matching algorithm with relevance feedback for brain MRI , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[9]  Xiangyang Wang,et al.  A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM parameter estimation , 2011, Appl. Soft Comput..

[10]  Francesco G. B. De Natale,et al.  A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization , 2010, IEEE Transactions on Multimedia.

[11]  Thomas S. Huang,et al.  Optimizing learning in image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  Lei Guo,et al.  A Bayesian Network Approach in the Relevance Feedback of Personalized Image Semantic Model , 2011 .

[13]  E. Wilson,et al.  Sociobiology: The New Synthesis , 1975 .

[14]  Gerald Salton,et al.  Automatic text processing , 1988 .

[15]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[16]  Chengwen Zhang,et al.  Content-Based Remote Sensing Image Retrieval Using Image Multi-feature Combination and SVM-Based Relevance Feedback , 2012 .