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Sign text detection in street view images using an integrated feature

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Abstract

Based on Bag of Visual Words (BoVWs) model, this paper proposes a novel method using an integrated feature to detect sign text in the street view images. BRISK features are first extracted from the street view images for dictionary learning. The Self-Growing and Self-Organized Neural Gas (SGONG) network is then used to cluster adaptively the extracted BRISK descriptors for generating visual words. The histogram of visual words is further calculated to form the appearance feature of the sign text. For eliminating the color differences and further highlighting the histogram similarity of all colors of signs, a color invariant histogram, called CIHS histogram, is presented to represent the color information of the sign text. By integrating the visual words histograms and CIHS histograms, an integrated descriptor, called Appearance and Color (A&C) descriptor, is specifically designed as the input features for cascade-Adaboost classifier. In the multi-scale sliding window text sign detection, integral image is applied to the spatial distribution map of each visual word for avoiding repeated extraction of features. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods and the detectors with the traditional descriptors.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant No.61671376 and 61671374.

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Authors and Affiliations

  1. Department of Information Science, Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an, Shaanxi, 710048, People’s Republic of China

    Fan Zhao, Yao Yang, Hai-yan Zhang, Lin-lin Yang & Lin Zhang

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Correspondence to Fan Zhao.

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Zhao, F., Yang, Y., Zhang, Hy. et al. Sign text detection in street view images using an integrated feature. Multimed Tools Appl 77, 28049–28076 (2018). https://doi.org/10.1007/s11042-018-5975-8

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