Proceedings ArticleDOI
Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval
Devraj Mandal,Kunal N. Chaudhury,Soma Biswas +2 more
- pp 2633-2641
TLDR
This paper proposes a simple, yet effective generalized hashing framework which can work for all the different scenarios, while preserving the semantic distance between the data points, and learns the optimum hash codes for the two modalities simultaneously.Abstract:Â
Due to availability of large amounts of multimedia data, cross-modal matching is gaining increasing importance. Hashing based techniques provide an attractive solution to this problem when the data size is large. Different scenarios of cross-modal matching are possible, for example, data from the different modalities can be associated with a single label or multiple labels, and in addition may or may not have one-to-one correspondence. Most of the existing approaches have been developed for the case where there is one-to-one correspondence between the data of the two modalities. In this paper, we propose a simple, yet effective generalized hashing framework which can work for all the different scenarios, while preserving the semantic distance between the data points. The approach first learns the optimum hash codes for the two modalities simultaneously, so as to preserve the semantic similarity between the data points, and then learns the hash functions to map from the features to the hash codes. Extensive experiments on single label dataset like Wiki and multi-label datasets like NUS-WIDE, Pascal and LabelMe under all the different scenarios and comparisons with the state-of-the-art shows the effectiveness of the proposed approach.read more
Citations
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Proceedings ArticleDOI
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
TL;DR: Li et al. as discussed by the authors proposed a self-supervised adversarial hashing (SSAH) approach, which leveraged two adversarial networks to maximize the semantic correlation and consistency of the representations between different modalities.
Posted Content
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
TL;DR: Li et al. as discussed by the authors proposed a self-supervised adversarial hashing (SSAH) approach, which leveraged two adversarial networks to maximize the semantic correlation and consistency of the representations between different modalities.
Proceedings ArticleDOI
Zero-Shot Sketch-Image Hashing
TL;DR: Zhang et al. as discussed by the authors proposed a zero-shot sketch-image hashing (ZSIH) model with an end-to-end three-network architecture, two of which are treated as binary encoders and the third network mitigates the sketch image heterogeneity and enhances the semantic relations among data.
Posted Content
Zero-Shot Sketch-Image Hashing
TL;DR: ZSIH is the first zero- shot hashing work suitable for SBIR and cross-modal search and forms a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.
Journal ArticleDOI
Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval
TL;DR: The semi-supervised model named adaptive semi- supervised feature selection for cross-modal retrieval uses the semantic regression to strengthen the neighboring relationship between the data with the same semantic and an efficient joint optimization algorithm is proposed to update the mapping matrices and the label matrix for unlabeled data simultaneously and iteratively.
References
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