13 January 2018 Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification
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Abstract
Saliency detection has been applied to the target acquisition case. This paper proposes a two-dimensional hidden Markov model (2D-HMM) that exploits the hidden semantic information of an image to detect its salient regions. A spatial pyramid histogram of oriented gradient descriptors is used to extract features. After encoding the image by a learned dictionary, the 2D-Viterbi algorithm is applied to infer the saliency map. This model can predict fixation of the targets and further creates robust and effective depictions of the targets’ change in posture and viewpoint. To validate the model with a human visual search mechanism, two eyetrack experiments are employed to train our model directly from eye movement data. The results show that our model achieves better performance than visual attention. Moreover, it indicates the plausibility of utilizing visual track data to identify targets.
© 2018 SPIE and IS&T
Weibing Wan, Weibing Wan, Lingfeng Yuan, Lingfeng Yuan, Qunfei Zhao, Qunfei Zhao, Tao Fang, Tao Fang, } "Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification," Journal of Electronic Imaging 27(1), 013006 (13 January 2018). https://doi.org/10.1117/1.JEI.27.1.013006 . Submission: Received: 1 September 2017; Accepted: 22 December 2017
Received: 1 September 2017; Accepted: 22 December 2017; Published: 13 January 2018
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