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Automatic image annotation using fuzzy association rules and decision tree

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Abstract

The problem of sharp boundary widely exists in image classification algorithms that use traditional association rules. This problem makes classification more difficult and inaccurate. On the other hand, massive image data will produce a lot of redundant association rules, which greatly decrease the accuracy and efficiency of image classification. To relieve the influence of these two problems, this paper proposes a novel approach integrating fuzzy association rules and decision tree to accomplish the task of automatic image annotation. According to the original features with membership functions, the approach first obtains fuzzy feature vectors, which can describe the ambiguity and vagueness of images. Then fuzzy association rules are generated from fuzzy feature vectors with fuzzy support and fuzzy confidence. Fuzzy association rules can capture correlations between low-level visual features and high-level semantic concepts of images. Finally, to tackle the large size of fuzzy association rules base, we adopt decision tree to reduce the unnecessary rules. As a result, the algorithm complexity is decreased to a large extent. We conduct the experiments on two baseline datasets, i.e. Corel5k and IAPR-TC12. The evaluation measures include precision, recall, F-measure and rule number. The experimental results show that our approach performs better than many state-of-the-art automatic image annotation approaches.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 61663004, 61262005, 61363035, 61365009, 61165009), the Guangxi Natural Science Foundation (2013GXNSFAA019345, 2014GXNSFAA118368), the National Basic Research Program of China (No. 2012CB326403) and the Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Zhixin Li, Lingzhi Li, Kaobi Yan & Canlong Zhang

  2. Guangxi Experiment Center of Information Science, Guilin, 541004, China

    Zhixin Li & Canlong Zhang

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  1. Zhixin Li
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  2. Lingzhi Li
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  3. Kaobi Yan
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  4. Canlong Zhang
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Correspondence to Zhixin Li.

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Li, Z., Li, L., Yan, K. et al. Automatic image annotation using fuzzy association rules and decision tree. Multimedia Systems 23, 679–690 (2017). https://doi.org/10.1007/s00530-016-0530-9

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