Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization
Shanghai Jiao Tong University · Nanjing University
Abstract
Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing~(SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method,…
Citation impact
- FWCI
- 29.14
- Percentile
- 100%
- References
- 71
Authors
2Topics & keywords
- Computer science
- Scalability
- Hash function
- Semantic similarity
- Maximization
- Machine learning
- Artificial intelligence
- Information retrieval