Fast action retrieval from videos via feature disaggregation

Learning based hashing methods, which aim at learning similarity-preserving binary codes for efficient nearest neighbor search, have been actively studied recently. A majority of the approaches address hashing problems for image collections. However, due to the extra temporal information, videos are usually represented by much higher dimensional (thousands or even more) features compared with images, causing high computational complexity for conventional hashing schemes. In this paper, we propose a simple and efficient hashing scheme for high-dimensional video data. This method, called Disaggregation Hashing (DH), exploits the correlations among different feature dimensions. An intuitive feature disaggregation method is first proposed, followed by a novel hashing algorithm based on different feature clusters. Additionally, a kernelized version of DH is proposed for better performance. We demonstrate the efficiency and effectiveness of our method by theoretical analysis and exploring its application on action retrieval from video databases. Extensive experiments show the superiority of our binary coding scheme over state-of-the-art hashing methods.