Abstract
In recent years, convolutional neural networks (CNNs) have achieved remarkable success in computer vision applications. Deep hashing combines feature extraction or representation with hash coding jointly; it can extract high-quality image features and generate approximate hash codes containing rich semantic information. Because an image is represented by binary codes instead of a high-dimensional floating-point-number feature matrix, the hashing method can significantly improve the speed of large-scale image retrieval. However, we notice that compared with traditional retrieval methods, image retrieval through binary hash encoding induces performance degradation to a certain extent, and most existing hash retrieval algorithms focus only on the semantic similarity between image pairs, the returned image samples should not only match the ground truth, but also the correct image should be in the front of the result list, they ignore the ranking information of the returned samples, limiting their performance. For this issue, this paper proposes a multimodel ensemble image retrieval framework which can learn compact hash codes containing rich semantic information through hash constraints. The ensemble strategy is introduced, and the weighted voting is applied to integrate the ranking list. Comprehensive experiments on three benchmark datasets show that the proposed method achieves very competitive results.
Supported by Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62176033 and 61936001, the Natural Science Foundation of Chongqing under Grant No. cstc2019jcyj-msxmX0380.
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Li, D., Dai, D., Shan, H., Xia, S., Xia, Y. (2022). Ensemble Ranking for Image Retrieval via Deep Hash. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_53
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