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An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion

College of Sciences, Northeastern University, Shenyang 110819, China
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Entropy 2018, 20(8), 577; https://doi.org/10.3390/e20080577
Received: 23 June 2018 / Revised: 30 July 2018 / Accepted: 31 July 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Entropy in Image Analysis)
With the rapid development of information storage technology and the spread of the Internet, large capacity image databases that contain different contents in the images are generated. It becomes imperative to establish an automatic and efficient image retrieval system. This paper proposes a novel adaptive weighting method based on entropy theory and relevance feedback. Firstly, we obtain single feature trust by relevance feedback (supervised) or entropy (unsupervised). Then, we construct a transfer matrix based on trust. Finally, based on the transfer matrix, we get the weight of single feature through several iterations. It has three outstanding advantages: (1) The retrieval system combines the performance of multiple features and has better retrieval accuracy and generalization ability than single feature retrieval system; (2) In each query, the weight of a single feature is updated dynamically with the query image, which makes the retrieval system make full use of the performance of several single features; (3) The method can be applied in two cases: supervised and unsupervised. The experimental results show that our method significantly outperforms the previous approaches. The top 20 retrieval accuracy is 97.09%, 92.85%, and 94.42% on the dataset of Wang, UC Merced Land Use, and RSSCN7, respectively. The Mean Average Precision is 88.45% on the dataset of Holidays. View Full-Text
Keywords: image retrieval; multi-feature fusion; entropy; relevance feedback image retrieval; multi-feature fusion; entropy; relevance feedback
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Lu, X.; Wang, J.; Li, X.; Yang, M.; Zhang, X. An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion. Entropy 2018, 20, 577.

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