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Multi-modality, the unique and important property of video data, is typically ignored in existing video adaptation processes. To solve this problem, we propose a novel approach, named multi-modality transfer based on multi- graph optimization (MMT-MGO) in this paper, which leverages multi-modality knowledge generalized by auxiliary classifiers in the source domain to assist multi-graph optimization...
The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases under controlled environments or focus on excessively localized features without sufficiently encapsulating the spatio-temporal context. In this paper, we propose to model the...
The paper originally presents a confusion network based framework for video OCR post-processing. The framework consists of four parts: selection of reference and hypotheses, construction of confusion network, decoding for final output, and a novel metric of quantitatively evaluating Video OCR post-processing approaches. By integrating both visual and textual information, we construct the character...
Large scale video copy detection task requires compact feature insensitive to various copy changes. Based on local feature trajectory behavior we discover invariant visual patterns for generating robust feature. Bag of Trajectory (BoT) technical is adopted for fast pattern matching. Our algorithm with lower cost is more robust compared to the state-of-art schemes.
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...
The paper presents a novel tempo model for movie content analysis. We originally propose that tempo indicates the rhythm of both movie scenarios and human perception and focus on the low level features extraction to represent both aspects. By thoroughly analyzing them, 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 shot...
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...
For conveniently navigating and editing the news programs, it is very important to segment the video into meaningful units. The effective indexing of news videos can be fulfilled by the anchorperson shot because it is an indicator which denotes the occurrence of upcoming news stories. The paper presents a novel anchorperson detection algorithm based on spatio-temporal slice (STS). With STSpattern...
In this paper, we propose a motion adaptive deinterlacing method with texture detection for scan-rate conversion of video data. The basic idea of this method is to classify the missing pixel to four different regions including motion smooth region, motion texture region, static smooth region, and static texture region based on the results of motion detection and texture detection, then four different...
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