Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm

BI Xiao-jun, PAN Tie-wen

ACTA ELECTRONICA SINICA ›› 2017, Vol. 45 ›› Issue (7) : 1668-1676.

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ACTA ELECTRONICA SINICA ›› 2017, Vol. 45 ›› Issue (7) : 1668-1676. DOI: 10.3969/j.issn.0372-2112.2017.07.017

Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm

  • BI Xiao-jun, PAN Tie-wen
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Abstract

To improve the performance of image retrieval,and accelerate the speed of image retrieval in content-based image retrieval and reduce the "semantic gap" between visual low-level features and high-level semantic,relevance feedback image retrieval based on teaching-learning-based optimization algorithm is proposed (TLBO-RF).Considering the specificity of image retrieval and the advantage of the PSO,the update strategy of individual is modified in TLBO,the center of the relevant images is regarded as the teacher and the personal best is introduced,which makes the algorithm converge fast to the region of relevant images that the user is interested in.TLBO-RF is compared to two state-of-the-art RFs based on evolutionary algorithm on two benchmark images.The results show that TLBO-RF has obvious advantage in comparison with other two algorithms,not only increases the performance of image retrieval,but also improves the image retrieval speed,and can better meet the user needs of image retrieval.

Key words

content-based image retrieval / relevance feedback / TLBO / PSO

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BI Xiao-jun, PAN Tie-wen. Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm[J]. Acta Electronica Sinica, 2017, 45(7): 1668-1676. https://doi.org/10.3969/j.issn.0372-2112.2017.07.017

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Funding

National Natural Science Foundation of China (No.61175126); National International Science and Technology Cooperation Project (No.2015DFG12150)
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