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Video scene segmentation and classification are fundamental steps for multimedia retrieval, browsing and indexing. In this paper, we present a robust scene segmentation approach based on the Markov Chain Monte Carlo (MCMC) method using the structure of video sequences. In our method, there are two novel approaches to segment video sequences into scenes. The first approach is the use of the video structures...
Most current research on human action recognition in videos uses the bag-of-words (BoW) representations based on vector quantization on local spatial temporal features, due to the simplicity and good performance of such representations. In contrast to the BoW schemes, this paper explores a localized, continuous and probabilistic video representation. Specifically, the proposed representation encodes...
In this paper, we specifically propose the Weber-Fechner Law-based human attention model for semantic scene analysis in movies. Different from traditional video processing techniques, we pay more attention on bringing in the related subjects, such as psychology, physiology and cognitive informatics, for content-based video analysis. The innovation of our work has two aspects. Firstly, we originally...
In this paper, we present an innovative model of tempo and its application in action scene detection for movie analysis. For the first time, we clearly propose that tempo indicates the rhythm of both movie scenarios and human perception. By thoroughly analyzing both aspects, we classify the factors of tempo into two sorts. The first is based on the film grammar and we use the low level features of...
Eager learning methods, such as SVM, are widely applied in video annotation task for their substantial performance. However, their computational costs are usually prohibitive when a large dataset is faced, especially when annotating a large lexicon of semantic concepts. This paper proposes a video annotation scheme based on lazy learning, and shows that this scheme is much more computationally efficient...
In this paper, we present a novel framework for video semantic detection based on transductive inference and hierarchical clustering, which directly focuses on predicting the available samples in a current unlabeled pool, instead of trying to build a classifier workable for any unavailable data. In this framework, a number of hierarchical clustering results are constructed from the entire video dataset ...
Active learning and semi-supervised learning methods are frequently applied in multimedia annotation tasks in order to reduce human labeling effort. However, in most of these methods only single modality is applied. This paper presents an interactive video annotation framework, which is based on semi-supervised learning and active learning with multiple multimodalities. In the proposed framework,...
Given a large set of video database, how to connect video segments with a certain set of semantic concepts with least manual labors is an elementary step for video indexing and searching. Due to the large gap between high-level semantics and low-level features, automatic video annotation with high accuracy is a challenging task. In this paper, we propose a novel automatic video annotation framework,...
As there is a large gap between high-level semantics and low-level features, it is difficult to obtain high-accuracy video semantic annotation through automatic methods. In this paper, we propose a novel automatic video annotation method, which greatly improves the annotation performance by learning from unlabeled video data, as well as exploring temporal consistency of video sequences. To effectively...
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