Spectral multimodal hashing and its application to multimedia retrieval

In recent years, multimedia retrieval has sparked much research interest in the multimedia, pattern recognition, and data mining communities. Although some attempts have been made along this direction, performing fast multimodal search at very large scale still remains a major challenge in the area. While hashing-based methods have recently achieved promising successes in speeding-up large-scale similarity search, most existing methods are only designed for uni-modal data, making them unsuitable for multimodal multimedia retrieval. In this paper, we propose a new hashing-based method for fast multimodal multimedia retrieval. The method is based on spectral analysis of the correlation matrix of different modalities. We also develop an efficient algorithm that learns some parameters from the data distribution for obtaining the binary codes. We empirically compare our method with some state-of-the-art methods on two real-world multimedia data sets.