Abstract
Deep supervised hashing methods typically learn hash functions by leveraging annotated similarities between images. Among these similarity measures, the central similarity quantization (CSQ) and its successors have excelled in optimizing the similarity between images w.r.t. their hash centers, achieving state-of-the-art performance. Nevertheless, these methods often overlook the intricate relationships and topologies inherent between different categories in generating hash centers. This limitation can impact their performance in multi-label datasets and reduce the accuracy of the generated hash codes. To address this issue, we introduce a novel deep hashing approach that incorporates label correlations using a graph convolutional network. This approach enables us to generate hash centers that effectively capture the co-occurrence correlation among diverse categories. We have conducted extensive experiments on publicly available multi-label datasets, demonstrating that our proposed method outperforms competing techniques, achieving significant performance gains.
Supported by the National Key Research and Development Program of China under Grant 2023YFA1008502, the Natural Science Foundation of China under Grant 62102059, the Post-Doctoral Fellowship Program of CPSF under Grant GZB20230969, China Post-Doctoral Science Foundation under Grant 2023M740939, and the Heilongjiang Post-Doctoral Foundation under Grant LBH-Z23170.
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Fu, Y., Wan, Z., Yao, J., Li, Z. (2025). Label-Correlation Adaptive Central Similarity Hashing for Multi-label Image Retrieval. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15039. Springer, Singapore. https://doi.org/10.1007/978-981-97-8692-3_11
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