Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization

Authors

  • Dongqing Zhang Shanghai Jiao Tong University
  • Wu-Jun Li Nanjing University

DOI:

https://doi.org/10.1609/aaai.v28i1.8995

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, called semantic correlation maximization~(SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.

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Published

2014-06-21

How to Cite

Zhang, D., & Li, W.-J. (2014). Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8995

Issue

Section

Main Track: Novel Machine Learning Algorithms