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Adaptive multi-feature fusion via cross-entropy normalization for effective image retrieval
Information Processing & Management ( IF 7.4 ) Pub Date : 2022-10-28 , DOI: 10.1016/j.ipm.2022.103119
Wentao Ma , Tongqing Zhou , Jiaohua Qin , Xuyu Xiang , Yun Tan , Zhiping Cai

Multi-feature fusion has achieved gratifying performance in image retrieval. However, some existing fusion mechanisms would unfortunately make the result worse than expected due to the domain and visual diversity of images. As a result, a burning problem for applying feature fusion mechanism is how to figure out and improve the complementarity of multi-level heterogeneous features. To this end, this paper proposes an adaptive multi-feature fusion method via cross-entropy normalization for effective image retrieval. First, various low-level features (e.g., SIFT) and high-level semantic features based on deep learning are extracted. Under each level of feature representation, the initial similarity scores of the query image w.r.t. the target dataset are calculated. Second, we use an independent reference dataset to approximate the tail of the attained initial similarity score ranking curve by cross-entropy normalization. Then the area under the ranking curve is calculated as the indicator of the merit of corresponding feature (i.e., a smaller area indicates a more suitable feature.). Finally, fusion weights of each feature are assigned adaptively by the statistically elaborated areas. Extensive experiments on three public benchmark datasets have demonstrated that the proposed method can achieve superior performance compared with the existing methods, improving the metrics mAP by relatively 1.04% (for Holidays), 1.22% (for Oxf5k) and the N-S by relatively 0.04 (for UKbench), respectively.



中文翻译:

通过交叉熵归一化的自适应多特征融合用于有效的图像检索

多特征融合在图像检索中取得了可喜的成绩。然而,不幸的是,由于图像的域和视觉多样性,一些现有的融合机制会使结果比预期的更差。因此,应用特征融合机制的一个紧迫问题是如何找出和提高多层次异构特征的互补性。为此,本文提出了一种通过交叉熵归一化的自适应多特征融合方法,用于有效的图像检索。首先,提取各种基于深度学习的低级特征(如SIFT)和高级语义特征。在每一级特征表示下,计算查询图像与目标数据集的初始相似度得分。第二,我们使用独立的参考数据集通过交叉熵归一化来近似获得的初始相似度得分排名曲线的尾部。然后计算排序曲线下的面积作为相应特征的优劣指标(即面积越小表示特征越合适)。最后,每个特征的融合权重由统计详细的区域自适应地分配。在三个公共基准数据集上的大量实验表明,与现有方法相比,所提出的方法可以实现卓越的性能,将指标 mAP 提高了相对 1.04%(对于 Holidays),1.22%(对于 Oxf5k)和 NS 相对提高了 0.04(对于UKbench),分别。然后计算排序曲线下的面积作为相应特征的优劣指标(即面积越小表示特征越合适)。最后,每个特征的融合权重由统计详细的区域自适应地分配。在三个公共基准数据集上的大量实验表明,与现有方法相比,所提出的方法可以实现卓越的性能,将指标 mAP 提高了相对 1.04%(对于 Holidays),1.22%(对于 Oxf5k)和 NS 相对提高了 0.04(对于UKbench),分别。然后计算排序曲线下的面积作为相应特征的优劣指标(即面积越小表示特征越合适)。最后,每个特征的融合权重由统计详细的区域自适应地分配。在三个公共基准数据集上的大量实验表明,与现有方法相比,所提出的方法可以实现卓越的性能,将指标 mAP 提高了相对 1.04%(对于 Holidays),1.22%(对于 Oxf5k)和 NS 相对提高了 0.04(对于UKbench),分别。

更新日期:2022-10-29
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