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In this paper a particular issue is addressed: matching still images (screen shots) with videos. A content-based similarity search approach using image queries is proposed. A fast method based on local visual patterns both for matching and indexing is employed. But we argue that using every frames may limit the scalability of the approach. Therefore only keyframes are extracted and used. The main...
For efficient indexing and retrieval of video archives, concept detection stands as an important problem. In this work, a generalized structure that can be used for detection of diverse and distinct concepts is proposed. In the system, MPEG-7 Descriptors and Scale Invariant Transform (SIFT) are utilized as visual features. Furthermore, visual features are transformed by codebooks which are constructed...
Tracking is a major issue of virtual and augmented reality applications. Single object tracking on monocular video streams is fairly well understood. However, when it comes to multiple objects, existing methods lack scalability and can recognize only a limited number of objects. Thanks to recent progress in feature matching, state-of-the-art image retrieval techniques can deal with millions of images...
In outsized multimedia databases video segmentation is a fundamental constituent necessary to assist proficient content based retrieval and browsing of visual information. This paper presents work towards an integrated framework for computerized video shot detection. In this framework a set of representative key frames are selected which helps in summarizing of the entire video content into an abstract...
This paper proposes a method of story segmentation for news video using multimodal analysis. The method detects the topic-caption frames, integrates them with silence clips detection, shot segmentation to locate news story boundaries. On test data with 135,400 frames, the accuracy rate 87.9% and the recall rate 98.7% are obtained. The experimental results show the method is promising and robust.
Video scenes provide semantic meanings for video content description and summarization. This paper explores the pair-wise visual cues of near-duplicate objects for link-constraint affinity-propagation without using keyframes. Experiments demonstrate that our method is more capable to identify scenes comparing with non-constrained clustering algorithms.
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