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Object detection based on saturation of visual perception

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Abstract

This paper presents a framework to detect salient objects from natural images through simulating human perception. We think perception saturation, generated by microsaccades in gaze, is the primary reason why human brains export consciousness of salient objects. Perception saturation is represented by using amplitude of microsaccades (AOM). When the AOM or changes of the AOM tend to zero, human perception becomes saturated. Motivated by this analysis, we construct a group of learning-based models to detect a salient object in a coarse-to-fine sequence. Firstly, a small image is selected to minimize the AOM so as to saturate the perception. Then, neural networks with random weights (NNRW) are chosen to simulate the received visual stimuli. In order to examine the changes of AOM, a positive feedback loop is constructed which executes the procedure of “pixel sampling-learning classification” iteratively. The final fixation area after iterations is regarded as a salient object. The proposed algorithm is based on unsupervised learning and data-driven completely. Our results based on open image datasets show that the proposed method achieves better performance compared to those existing unsupervised algorithms.

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Acknowledgements

This research project was supported by the Natural Science Foundation of Zhejiang Province of China (No. LY19F030013).

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Authors and Affiliations

  1. China Jiliang University, Hangzhou, 310018, Zhejiang, China

    Chen Pan

  2. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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  1. Chen Pan
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Correspondence to Wei Qi Yan.

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Pan, C., Yan, W.Q. Object detection based on saturation of visual perception. Multimed Tools Appl 79, 19925–19944 (2020). https://doi.org/10.1007/s11042-020-08866-x

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  • DOI: https://doi.org/10.1007/s11042-020-08866-x

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