Abstract
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.
Highlights
(1) Based on entropy, we weigh features to improve the accuracy of similarity measurement; (2) Under the supervised condition, we obtain the single feature weight based on related feedback and fuse multi-feature at the measurement level to improve the retrieval precision; (3) Under the unsupervised condition, we obtain the single feature weight based on entropy and fuse multiple features at the measurement level to improve the retrieval precision
This paper proposes a method to obtain single feature weights to fuse multiple features for image retrieval
Retrieval results on daily scene datasets, which are Holidays and Wang, and remote sensing datasets, which are UC Merced Land Use and RSSCN7, show that compared with single feature and fusing multiple features by averaging global weights and relevance feedback, our method has better retrieval performance
Summary
As an important carrier of information, it is significant to do efficient research with images [1,2,3,4,5,6].Large-scale image retrieval has vast applications in many domains such as image analysis, search of image over internet, medical image retrieval, remote sensing, and video surveillance [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]. In a previous paper [8], the authors improve reciprocal neighbor based graph fusion approach for feature fusion by the SVM prediction strategy, which increases the robustness of original graph fusion approach. In another past paper [9], the authors propose a graph-based query specific fusion approach where multiple retrieval sets are merged and are reranked by conducting a link analysis on a fused graph, which is capable of adaptively integrating the strengths of the retrieval methods using local or holistic features for Entropy 2018, 20, 577; doi:10.3390/e20080577 www.mdpi.com/journal/entropy
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