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Human fixation detection model in video compressed domain based on Markov random field

[+] Author Affiliations
Yongjun Li

Xidian University, State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, No. 2, South Taibai Street, Hi-Tech Development Zone, Xi’an 710071, China

Xidian University, Joint Laboratory of High Speed Multi-Source Image Coding and Processing, School of Telecommunications Engineering, No. 2, South Taibai Street, Hi-Tech Development Zone, Xi’an 710071, China

Henan University, School of Physics and Electronics, No. 1 Jinming Street, Kaifeng, Henan 475004, China

Yunsong Li, Weijia Liu, Jing Hu, Chiru Ge

Xidian University, State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, No. 2, South Taibai Street, Hi-Tech Development Zone, Xi’an 710071, China

Xidian University, Joint Laboratory of High Speed Multi-Source Image Coding and Processing, School of Telecommunications Engineering, No. 2, South Taibai Street, Hi-Tech Development Zone, Xi’an 710071, China

J. Electron. Imaging. 26(1), 013008 (Jan 27, 2017). doi:10.1117/1.JEI.26.1.013008
History: Received August 11, 2016; Accepted December 19, 2016
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Abstract.  Recently, research on and applications of human fixation detection in video compressed domain have gained increasing attention. However, prediction accuracy and computational complexity still remain a challenge. This paper addresses the problem of compressed domain fixations detection in the videos based on residual discrete cosine transform coefficients norm (RDCN) and Markov random field (MRF). RDCN feature is directly extracted from the compressed video with partial decoding and is normalized. After spatial–temporal filtering, the normalized map [Smoothed RDCN (SRDCN) map] is taken to the MRF model, and the optimal binary label map is obtained. Based on the label map and the center saliency map, saliency enhancement and nonsaliency inhibition are done for the SRDCN map, and the final SRDCN-MRF salient map is obtained. Compared with the similar models, we enhance the available energy functions and introduce an energy function that indicates the positional information of the saliency. The procedure is advantageous for improving prediction accuracy and reducing computational complexity. The validation and comparison are made by several accuracy metrics on two ground truth datasets. Experimental results show that the proposed saliency detection model achieves superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics. Computationally, our algorithm reduces 26% more computational complexity with comparison to similar algorithms.

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Topics

Video ; Algorithms

Citation

Yongjun Li ; Yunsong Li ; Weijia Liu ; Jing Hu and Chiru Ge
"Human fixation detection model in video compressed domain based on Markov random field", J. Electron. Imaging. 26(1), 013008 (Jan 27, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013008


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