Semi-automatic video semantic annotation based on active learning

Yan Song; Xian-Sheng Hua; Li-Rong Dai; Ren-Hua Wang

doi:10.1117/12.631380

24 June 2005 Semi-automatic video semantic annotation based on active learning

Yan Song, Xian-Sheng Hua, Li-Rong Dai, Ren-Hua Wang

Proceedings Volume 5960, Visual Communications and Image Processing 2005; 59600R (2005) https://doi.org/10.1117/12.631380
Event: Visual Communications and Image Processing 2005, 2005, Beijing, China

Abstract

In this paper, we propose a novel semi-automatic annotation scheme for home videos based on active learning. It is well-known that there is a large gap between semantics and low-level features. To narrow down this gap, relevance feedback has been introduced in a number of literatures. Furthermore, to accelerate the convergence to the optimal result, several active learning schemes, in which the most informative samples are chosen to be annotated, have been proposed in literature instead of randomly selecting samples. In this paper, a representative active learning method is proposed, which local consistency of video content is effectively taken into consideration. The main idea is to exploit the global and local statistical characteristics of videos, and the temporal relationship between shots. The global model is trained on a smaller pre-labeled video dataset, and the local information is obtained online in the process of active learning, and will be used to adjust the initial global model adaptively. The experiment results show that the proposed active learning scheme has significantly improved the annotation performance compared with random selecting and common active learning method.

Citation Download Citation

Yan Song, Xian-Sheng Hua, Li-Rong Dai, and Ren-Hua Wang "Semi-automatic video semantic annotation based on active learning", Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59600R (24 June 2005); https://doi.org/10.1117/12.631380

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