Lu Xiaojun, Zhang Libo, Niu Lei, Chen Qing, Wang Jianping
College of Sciences, North Eastern University, Shenyang 110819, China.
Entropy (Basel). 2021 Dec 12;23(12):1670. doi: 10.3390/e23121670.
In the era of big data, it is challenging to efficiently retrieve the required images from the vast amount of data. Therefore, a content-based image retrieval system is an important research direction to address this problem. Furthermore, a multi-feature-based image retrieval system can compensate for the shortage of a single feature to a certain extent, which is essential for improving retrieval system performance. Feature selection and feature fusion strategies are critical in the study of multi-feature fusion image retrieval. This paper proposes a multi-feature fusion image retrieval strategy with adaptive features based on information entropy theory. Firstly, we extract the image features, construct the distance function to calculate the similarity using the information entropy proposed in this paper, and obtain the initial retrieval results. Then, we obtain the precision of single feature retrieval based on the correlation feedback as the retrieval trust and use the retrieval trust to select the effective features automatically. After that, we initialize the weights of selected features using the average weights, construct the probability transfer matrix, and use the PageRank algorithm to update the initialized feature weights to obtain the final weights. Finally, we calculate the comprehensive similarity based on the final weights and output the detection results. This has two advantages: (1) the proposed strategy uses multiple features for image retrieval, which has better performance and more substantial generalization than the retrieval strategy based on a single feature; (2) compared with the fixed-feature retrieval strategy, our method selects the best features for fusion in each query, which takes full advantages of each feature. The experimental results show that our proposed method outperforms other methods. In the datasets of Corel1k, UC Merced Land-Use, and RSSCN7, the top10 retrieval precision is 99.55%, 88.02%, and 88.28%, respectively. In the Holidays dataset, the mean average precision (mAP) was 92.46%.
在大数据时代，从海量数据中高效检索所需图像具有挑战性。因此，基于内容的图像检索系统是解决这一问题的重要研究方向。此外，基于多特征的图像检索系统可以在一定程度上弥补单一特征的不足，这对于提高检索系统性能至关重要。特征选择和特征融合策略在多特征融合图像检索研究中至关重要。本文提出了一种基于信息熵理论的具有自适应特征的多特征融合图像检索策略。首先，我们提取图像特征，利用本文提出的信息熵构建距离函数来计算相似度，得到初始检索结果。然后，我们基于相关反馈获得单特征检索的精度作为检索信任度，并利用检索信任度自动选择有效特征。之后，我们使用平均权重初始化所选特征的权重，构建概率转移矩阵，并使用PageRank算法更新初始化的特征权重以获得最终权重。最后，我们基于最终权重计算综合相似度并输出检测结果。这有两个优点：（1）所提出的策略使用多个特征进行图像检索，与基于单一特征的检索策略相比，具有更好的性能和更强的泛化能力；（2）与固定特征检索策略相比，我们的方法在每个查询中选择最佳特征进行融合，充分利用了每个特征的优势。实验结果表明，我们提出的方法优于其他方法。在Corel1k、加州大学默塞德分校土地利用和RSSCN7数据集上，前10的检索精度分别为99.55%、88.02%和88.28%。在节假日数据集中，平均精度均值（mAP）为92.46%。
