Ensemble learning framework for image retrieval via deep hash ranking

Deep hashing combines feature extraction or representation with hash coding jointly, which can significantly improve the speed of large-scale image retrieval. However, we notice that compared with traditional retrieval methods, due to the reduction of dimension and information loss, the retrieval performance of binaryhash coding has declined to a certain extent. Most hash retrieval algorithms focus on the semantic similarity between image pairs, and ignore the ranking information between the returned samples. The returned samples should not only match the retrieved samples, but also rank the correct samples in front of the returned list. In addition, the performance difference of the deep model used in deep hash retrieval will also limit the efficiency of retrieval. To address such problem, we proposed an ensemble deep neural model robust framework for image retrieval, 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. Codes are available at https://github.com/lidonggen-123/Ensemble_Deephash_Image_Retrieval.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Deep hashing combines feature extraction or representation with hash coding jointly, which can significantly improve the speed of large-scale image retrieval. However, the retrieval performance of binary hash coding has declined to a certain extent due to the reduction of dimension and information loss compared with traditional retrieval methods.