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Consistent and Specific Hashing for image set classification
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AI
DOI：10.1016/j.neunet.2025.107462.png)
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Abstract
En 中文
Image set classification (ISC) has obtained extensive attention with the continuous development of information technology. Different from the traditional single image classification, ISC mainly utilizes image set to improve classification performance instead of an individual image, which could effectively utilize more information from the set to mitigate various appearance variations. Most existing ISC methods learn effective real-value latent discriminative representation, which could suffer from large computational complexity, thereby directly affecting the practical applications for large-scale data scenes. Recently, image hashing has emerged as an effective solution due to its low computational requirements. However, existing hashing based ISC methods often ignore consistency and specificity properties in image sets, which makes it difficult to explore the relationship information from different sets. To overcome the above limitation, we propose a novel Consistent and Specific Hashing (CSH) for ISC, which can effectively excavate semantic information from different sets. Specifically, we first construct Hadamard matrix as pre-computed set-consistent hash codes for each gallery set, thereby maximizing the Hamming distance between different sets. Then, we learn sample-specific hash codes for all images in whole gallery sets. Finally, we propose a hashing aggregation strategy to preserve intra-set semantic information well, thereby endowing sample-specific hash codes intra-set compactness and inter-set separability. Plenty of experiment results show that the promising performance of our proposed CSH compared with these comparison methods in the classification results and running time.
Keywords:
Image set classification
Sample-specific hash codes
Set-consistent hash codes
Hashing aggregation strategy
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