Field of experts optimization‐based noisy image retrieval. (4th February 2020)

• Record Type: Journal Article
• Authors: Guo, Junqi; Shen, Guicheng; Sun, Yichen; Zhao, Jin; Wu, Hao; Zhu, Zhilin
• Other Names: Wu Hao guestEditor.; Bie Rongfang guestEditor.; Pereira Charith guestEditor.; Rana Omer guestEditor.
• Is Part Of: Software, practice & experience. Volume 50:Number 11(2020)
• Page Start: 2110
• Page End: 2118
• Publication Date: 2020-02-04
• Subjects: FoE -- multioption filters -- noisy image retrieval -- sparse coding; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2788
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: The value of image retrieval has become more and more prominent in the era of big data. However, large numbers of images are missed from current method since the image retrieval precision largely depends on the high quality of images. By common methodology, when the quality of images decreases a little, the accuracy of retrieval would decrease significantly. In particular, it is difficult to retrieve noisy images effectively by conventional approach. Yet large number of the noisy images could not be ignored at the age of data explosion. Aiming at the problem above, we proposed noisy image retrieval model based on field of experts (FoE) optimization. High‐quality learning images could be selected by sparse coding, which is based on similarity calculation model, and the multioption filter combination model enhances the power of FoE model. We set up a database containing a large numbers of noisy images. Over this database, adequate groups of experiments are conducted. The verification of the method concluded its effectiveness and superiority.