Науковий вісник НГУ

An image retrieval and semantic mapping method based on region of interest

• 
• 

User Rating:  / 1

PoorBest 

Details

Category: Information technologies, systems analysis and administration

Last Updated on 08 February 2016

Published on 08 February 2016

Hits: 4558

Authors:

Gongwen Xu, Shandong Jianzhu University, Jinan, Shandong, China

Xiaomei Li, Shandong University, Jinan, Shandong, China

Jian Lei, Dongying Branch, Dongying, Shandong, China

Honglan Zhou, Dongying Branch, Dongying, Shandong, China

Abstract:

Purpose. In order to increase the precision and recall rate of image retrieval method, a new image retrieval method is proposed, which bases on the idea that different parts of an image have different discriminative capabilities and contribute to the image retrieval to different extents. The region of interest method was used to improve the effectiveness of the semantic mapping of content and image retrieval.

Methodology. Based on the region of interest, we got a semantic class range in the feature space by semi-supervised lear-ning. The semantic class range is the mapping of image and class. In view of the different image features, the most suitable distance measurement was chosen.

Findings. The region of interest was divided from image by the improved Harris detection method and extract the low-level image features. The features in database were used as training data for semi-supervised learning to build the mapping of images and semantic classes which is updated iteratively by the relevance feedback.

Originality. The map between an image region of interest and class labels was studied. Two kinds of distances were applied to calculate the different feature’s similarity. The semantic mapping is updated in real time. The research realizes a novel and comprehensive image retrieval system.

Practical value. Different needs of users were considered, and the very good experiment results have been received. The image retrieval method proposed in this paper is quicker and more effective than other methods.

References:

1. Kakade, S.R. and Kakade, N.R. (2013), “A Novel Approach to Link Semantic Gap between Images and Tags via Probabilistic Ranking”, In Computational Intelligence and Computing Research (ICCIC), IEEE International Conference on, 2013 IEEE, pp. 1−6.

2. Kavitha, C., Rao, D.B.P. and Govardhan, D.A. (2011), “Image Retrieval based on Color and Texture Features of the Image Sub-blocks”, International Journal of Computer Applications, vol. 15, no.7, pp. 33−37.

3. Stöttinger, J., Hanbury, A., Sebe, N. and Gevers, T. (2012), “Sparse Color Interest Points for Image Retrieval and Object Categorization”, Image Processing, IEEE Transactions on, vol. 21, no.5, pp. 2681−2692.

4. Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., and Acar, B. (2011), “Content-based Image Retrieval in Radiology: Current Status and Future Directions”, Journal of Digital Imaging, vol. 24, no.2, pp. 208− 222.

5. Xu, B., Li, Z. and Xie, S. (2013), “Rotation Invariant Texture Image Retrieval Based on Log-Polar and NSCT”, IEEE Communications Letters, vol. 72, no.46, pp.41−53.

6. Misra, I., Moorthi, S.M., Dhar, D. and Ramakrishnan, R.(2012), “An Automatic Satellite Image Registration Technique based on Harris Corner Detection and Random Sample Consensus (RANSAC) Outlier Rejection Model", In Recent Advances in Information Technology (RAIT), 2012 1st Internatinal Conference on, 2012, IEEE, pp.68−73.

7. Reddy, P., Reddy, A. and Devi, K. (2012), “HSV Color Histogram and Directional Binary Wavelet Patterns for Content based Image Retrieval”, International Journal on Computer Science and Engineering , vol. 4, no.08, pp. 1402 −1411.

8. Singha, M. and Hemachandran, K. (2012), “Content based Image Retrieval Using Color and Texture”, Signal &Image Processing: An International Journal (SIPIJ), vol. 3, no.1, pp. 39−57.

9. Liu, G.H. and Yang, J.Y. (2013), “Content-based image retrieval using color difference histogram”, Pattern Recognition, vol. 46, no.1, pp. 188−198.

10. Wang, X. and Wang, Z. (2013), “A Novel Method for Image Retrieval based on Structure Elements’ Descriptor”, Joural of Visual Communication and Image Representation, vol. 24, no.1, pp. 63−74.

2015_06_xu
2016-02-08  851.97 KB  974

• < Prev
• Next >