Fusion Visual Attention and Low-Level Features in Images for Region of Interest Extraction

CHEN Zailiang; ZOU Beiji; GAO Xu; SHEN Hailan; ZHANG Xiaoyun

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 287-290

CHEN Zailiang, ZOU Beiji, GAO Xu, et al., “Fusion Visual Attention and Low-Level Features in Images for Region of Interest Extraction,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 287-290, 2013,

Citation:

CHEN Zailiang, ZOU Beiji, GAO Xu, et al., “Fusion Visual Attention and Low-Level Features in Images for Region of Interest Extraction,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 287-290, 2013,

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Fusion Visual Attention and Low-Level Features in Images for Region of Interest Extraction

School of Information Science and Engineering, Central South University, Changsha 410083, China

Funds: This work is supported by the Youth Research Funds of School of Information Science and Engineering, Central South University (No.2011170314), Hunan Provincial Natural Science Foundation of China (No.11JJ3067, No.10JJ6088), the National Natural Science Foundation of China (No.60970098, No.60903136, No.61173122), Fundamental Research Funds for the Central Universities (No.2012QNZT067).

Received Date: 2011-12-01
Rev Recd Date: 2011-12-01
Publish Date: 2013-04-25

Abstract

Abstract

For the point of Region of interest (ROI) extraction influenced by low-level features in images, this paper proposes two ROI extraction algorithms. The first one is based on eye movement data and use proposed “Marking Helm” method. The second one is an optimal weighted feature ROI extraction algorithm based on performance evaluation of feature ROI with different low-level features such as color, intensity, orientation and texture feature in images. By analyzing the similarity between the extracted ROIs by the proposed optimal weighted feature ROI extraction algorithm, visual attention models and eye movement data respectively, this paper confirms the validity of the proposed algorithm. Experimental results show that the proposed optimal weighted feature ROI extraction algorithm improves the similarity at least ten percent over the ROI extraction algorithm based on Itti and Stentiford visual attention models.
- Region of interest (ROI),
- Low-level feature,
- Eye movement,
- Similarity

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References(10)

References

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