MuseHash: Supervised Bayesian Hashing for Multimodal Image Representation
Creators
- 1. Information Technologies Institute Centre of Research & Technology - Hellas
- 2. Reykjavik University
Description
This paper presents a novel method for supporting multiple modalities in the field of image retrieval, called Multimodal Bayesian Supervised Hashing (MuseHash). The method takes into consideration the semantic information of the training data through the use of Bayesian regression to estimate the semantic probabilities and statistical properties in the retrieval process. This method is an extension of the previously proposed Bayesian ridge-based Semantic Preserving Hashing (BiasHash) method. Experimentation on various domain-specific and benchmark datasets demonstrates that MuseHash outperforms six existing state-of-the-art methods in image retrieval performance, regardless of the feature extractor type, code length, and visual or textual descriptors used. This highlights the robustness and adaptability of MuseHash, making it a promising solution for multimodal image retrieval.
Files
ICMR2023___Maria_Pegia.pdf
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Additional details
Funding
- CALLISTO – Copernicus Artificial Intelligence Services and data fusion with other distributed data sources and processing at the edge to support DIAS and HPC infrastructures 101004152
- European Commission
- ISOLA – Innovative & Integrated Security System on Board Covering the Life Cycle of a Passenger Ships Voyage 883302
- European Commission