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Visual search difficulty prediction with image ROI information

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Abstract

Target recognition difficulty quantification and prediction using the search time for the human visual system to target an object is a challenging task, which can effectively guide the training of machine learning models such as target recognition and target location. Our work focuses on how to use region-of-interest (ROI) information to improve the accuracy of the visual search difficulty prediction model. First, the influence of ROI information on visual search difficulty is explored in this paper. Then, based on the learning using privileged information paradigm, we build a support vector regression model using privileged information (SVR +), which uses the deep features of ROIs in the training stage. Next, a coordinate descent algorithm is developed to solve the dual optimization problem in SVR + training. Comprehensive experiments validate the improvement in the accuracy of the proposed model in predicting the difficulty of visual search and the efficiency of our coordinate descent algorithm in model training.

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Acknowledgements

This research was funded by China Postdoctoral Science Foundation, Grant Number 2020M673606XB

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Authors and Affiliations

  1. School of Optoelectronic Engineering, Xi’an Technological University, Xi’an , 710021, China

    Bo Xiao

  2. Information Perception and Intelligent Control Laboratory, Xi’an Technological University, Xi’an, 710021, China

    Xuelian Liu & Chunyang Wang

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  1. Bo Xiao
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  2. Xuelian Liu
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  3. Chunyang Wang
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Correspondence to Bo Xiao.

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Xiao, B., Liu, X. & Wang, C. Visual search difficulty prediction with image ROI information. Neural Comput & Applic 34, 6799–6809 (2022). https://doi.org/10.1007/s00521-021-06413-9

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