Adaptive multi-feature fusion via cross-entropy normalization for effective image retrieval. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Ma, Wentao; Zhou, Tongqing; Qin, Jiaohua; Xiang, Xuyu; Tan, Yun; Cai, Zhiping
• Is Part Of: Information processing & management. Volume 60:Issue 1(2023)
• Publication Date: 2023-01
• Subjects: Image retrieval -- Cross-entropy -- Feature fusion -- High-level semantic features; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.103119
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: 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. Highlights: We propose an efficient multi-feature fusion image retrieval method. Our method uses low-level and high-level semantic features. We use cross-entropy technique to improve the complementarity of features. This mechanism fuses the result of multi-feature in an unsupervised manner. Experimental results show that our method is highly competitive.