Recent Improvements in Image Retrieval

Abstract:

The target of this paper is to introduce the improvement of the technique of image retrieval. At first, it comes up with the concept of image retrieval and shows the importance of this technique. As the techniques of multimedia and Internet are developing rapidly, the resources of images that users obtain are also extended. And then this paper gives the problem about the image retrieval, namely the information of images are disordering. As the result, it is significant to do the effective organization, management and retrieval based on the increasingly extensive image information storage. After that, this paper presents the concept of TBIR and CBIR and gives the definitions of them. It proposes an issue that CBIR is the improvement of TBIR. Based on CBIR, there are also some disadvantages that need to be improved. In terms of the main point of CBIR, the paper raises that the annotation is one of the most difficult techniques that need to be promoted. Then it describes some algorithms about the technique of automatic image annotation. After these algorithms, the paper shows the challenges and developing direction of the technique of image retrieval. At last, it presented the conclusion to emphasize the main points of this paper.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 4069-4073
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.4069
• Online since: July 2014
• Authors: Yan Sheng Zeng*, Xu Lin Ying
• Keywords: Annotation, Automatic, CBIR, Image Retrieval, TBIR

References

[1] D. Liu, Efficient Techniques for Relevance Feedback Processing in Content-based Image Retrieval,. Florida, 2009, pp.7-41.

[2] Hernández-Gracidas, C. A., L. E. Sucar & M. Montes-y-Gómez Improving image retrieval by using spatial relations, Multimedia Tools and Applications, vol. 62, Nov. 2, pp.479-505, (2013).

DOI: 10.1007/s11042-011-0911-1

[3] D. Buffoni, S. Tollari & P. Gallinari, A Learning to Rank framework applied to text-image retrieval, Multimedia Tools and Applications, vol. 60, Nov. 1, pp.161-180, (2012).

DOI: 10.1007/s11042-011-0806-1

[4] O. Marques & B. Furht, MUSE: A content-based image search and retrieval system using relevance feedback, Multimedia Tools and Applications, vol. 17, Nov. 1, pp.21-50, (2002).

[5] Z. Shi, X. Liu, Q. Li, Q. He & Z. Shi , Extracting discriminative features for CBIR, Multimedia Tools and Applications, vol. 61, Nov. 2, pp.263-279, (2012).

DOI: 10.1007/s11042-011-0836-8

[6] R. Zhang, Combining visual features and contextual information for image retrieval and annotation, (2011).

[7] A. Boucher, Sub-pixel mapping of coarse satellite remote sensing images with stochastic simulations from training images, Mathematical geosciences, vol. 41, Nov. 3, pp.265-290, (2009).

DOI: 10.1007/s11004-009-9215-7

[8] M. Y. Fang, Y. H. Kuan, C. M. Kuo & C. H. Hsieh, Effective image retrieval techniques based on novel salient region segmentation and relevance feedback, Multimedia Tools and Applications, vol. 57, Nov. 3, pp.501-525, (2012).

DOI: 10.1007/s11042-010-0655-3

[9] I. Ahmad & M. Gabbouj, A generic content-based image retrieval framework for mobile devices, Multimedia Tools and Applications, vol. 55, Nov. 3, pp.423-442, (2011).

DOI: 10.1007/s11042-010-0556-5