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• W4386089418 abstract "Abstract Currently, there are various widely used methods for hashing-based cross modal retrieval to achieve promising performance in the information retrieval task. However, the challenge of learning discriminative representations against a heterogeneous gap in the case of multi-modal data still needs to be explored. Most existing methods directly project raw heterogeneous data linearly onto a common projection space, leading to high projection error because the semantic similarity of multi-modal data is not captured. We need the efficient feature representation of heterogeneous and multi-modal data to learn high-quality discrete hash codes and improve retrieval performance. This paper proposes a label-consistent kernel transform learning-based sparse hashing (LcKTLSH) for cross modal retrieval. Instead of projecting raw data samples, LcKTLSH learns transform and sparse coefficients suited for projection onto the kernel space, assuming each modality of a single instance has a unique hash code. The learned sparse coefficients are embedded into pre-learned hash codes, and semantic information from the similarity matrix is embedded into pre-learned hash codes to generate final hash codes. Kernel trick accounts for non-linearity in the data, and label-consistency constraint acts as the supervision term. Extensive experimental results on four benchmark datasets significantly advance over previous investigations." @default.
• W4386089418 created "2023-08-24" @default.
• W4386089418 creator A5061490660 @default.
• W4386089418 date "2023-08-24" @default.
• W4386089418 modified "2023-10-01" @default.
• W4386089418 title "Label-consistent Kernel Transform Learning based Sparse Hashing for cross-modal retrieval" @default.
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• W4386089418 doi "https://doi.org/10.21203/rs.3.rs-3284556/v1" @default.
• W4386089418 hasPublicationYear "2023" @default.
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