Zero-Shot Hashing via Asymmetric Ratio Similarity Matrix

Zero-shot hashing targets to learn the hash codes of images in unseen classes based on the limited training data provided by seen classes. In zero-shot hashing, transferring the supervised knowledge, such as attributes and semantic relations, from seen classes to unseen ones is a widely employed met...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 35; H. 5; S. 5426 - 5437
Hauptverfasser: Shi, Yang, Nie, Xiushan, Liu, Xingbo, Yang, Lu, Yin, Yilong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
asymmetric ratio similarity matrix
Asymmetry
Binary codes
Hamming distance
Hash functions
Hashing
Mathematical analysis
Optimization
Semantics
Similarity
Symmetric matrices
Visualization
zero-shot
ISSN:1041-4347, 1558-2191
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Zusammenfassung:Zero-shot hashing targets to learn the hash codes of images in unseen classes based on the limited training data provided by seen classes. In zero-shot hashing, transferring the supervised knowledge, such as attributes and semantic relations, from seen classes to unseen ones is a widely employed method, where the performance is always subject to the ability to capture these supervised knowledge (which is always difficult to obtain). Therefore, in this study, we propose a new methodology for zero-shot hashing via an asymmetric ratio similarity matrix (ASZH), which only needs to calculate the semantic similarity among seen classes for hash learning. Specifically, we use an asymmetric ratio matrix in the similarity calculation to further explore the influence of similarity, where the values of positive weights for similar samples are not equivalent to those of negative ones for dissimilar samples. Additionally, a theoretical analysis regarding the utilization of an asymmetric ratio matrix is provided in this study. The experiments on three large benchmark datasets indicate that the proposed method achieves excellent performance than several state-of-the-art hashing methods.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3150790