Lu Xiaojun, Wang Jiaojuan, Li Xiang, Yang Mei, Zhang Xiangde
College of Sciences, Northeastern University, Shenyang 110819, China.
Entropy (Basel). 2018 Aug 6;20(8):577. doi: 10.3390/e20080577.
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.
随着信息存储技术的快速发展和互联网的普及，产生了包含图像中不同内容的大容量图像数据库。建立一个自动高效的图像检索系统变得势在必行。本文提出了一种基于熵理论和相关反馈的新型自适应加权方法。首先，我们通过相关反馈（有监督）或熵（无监督）获得单特征可信度。然后，我们基于可信度构建一个转移矩阵。最后，基于转移矩阵，通过多次迭代得到单特征的权重。它具有三个突出优点：（1）该检索系统结合了多个特征的性能，比单特征检索系统具有更好的检索精度和泛化能力；（2）在每次查询中，单特征的权重随查询图像动态更新，这使得检索系统能够充分利用多个单特征的性能；（3）该方法可应用于有监督和无监督两种情况。实验结果表明，我们的方法明显优于先前的方法。在Wang、加州大学默塞德分校土地利用和RSSCN7数据集上，前20的检索准确率分别为97.09%、92.85%和94.42%。在Holidays数据集上的平均精度均值为88.45%。
