1. School of Information and Software Engineering, University of Electronic Science and Technology of China Chengdu 610054, China;
2. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China;
3. Center for Future Media, University of Electronic Science and Technology of China, Chengdu 611731, China;
4. School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 611731, China;
5. Sichuan University West China Hospital of Stomatology, Chengdu 610041, China;
6. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Abstract Video captioning is the task of assigning complex high-level semantic descriptions (e.g., sentences or paragraphs) to video data. Different from previous video analysis techniques such as video annotation, video event detection and action recognition, video captioning is much closer to human cognition with smaller semantic gap. However, the scarcity of captioned video data severely limits the development of video captioning. In this paper, we propose a novel video captioning approach to describe videos by leveraging freely-available image corpus with abundant literal knowledge. There are two key aspects of our approach: 1) effective integration strategy bridging videos and images, and 2) high efficiency in handling ever-increasing training data. To achieve these goals, we adopt sophisticated visual hashing techniques to efficiently index and search large-scale images for relevant captions, which is of high extensibility to evolving data and the corresponding semantics. Extensive experimental results on various real-world visual datasets show the effectiveness of our approach with different hashing techniques, e.g., LSH (locality-sensitive hashing), PCA-ITQ (principle component analysis iterative quantization) and supervised discrete hashing, as compared with the state-of-the-art methods. It is worth noting that the empirical computational cost of our approach is much lower than that of an existing method, i.e., it takes 1/256 of the memory requirement and 1/64 of the time cost of the method of Devlin et al.
This work was partially supported by the National Basic Research 973 Program of China under Grant No. 2014CB347600, the National Natural Science Foundation of China under Grant Nos. 61522203, 61572108, 61632007, and 61502081, the National Ten-Thousand Talents Program of China (Young Top-Notch Talent), the National Thousand Young Talents Program of China, the Fundamental Research Funds for the Central Universities of China under Grant Nos. ZYGX2014Z007 and ZYGX2015J055, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140058.
About author: Xiao-Yu Du is currently a lecturer in the School of Software Engineering of Chengdu University of Information Technology, Chengdu, and a Ph.D. candidate of University of Electronic Science and Technology of China, Chengdu. He received his M.E. degree in computer software and theory in 2011 and B.S. degree in computer science and technology in 2008, both from Beijing Normal University, Beijing. His research interests include multimedia analysis and retrieval, computer vision, and machine learning.
Cite this article:
Xiao-Yu Du, Yang Yang, Liu Yang, Fu-Min Shen, Zhi-Guang Qin, Jin-Hui Tang.Captioning Videos Using Large-Scale Image Corpus[J] Journal of Computer Science and Technology, 2017,V32(3): 480-493
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