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A video sequence usually consists of separate scenes, and each scene includes many shots. For video understanding purposes, it is most important to detect scene breaks. To analyze the content of each scene, detection of shot breaks is also required. Usually, a scene break is associated with a simultaneous change of image, motion, and audio characteristics,(More)
This paper describes a technique for classifying TV broadcast video using Hidden Markov Model (HMM) [1]. Here we consider the problem of discriminating five types of TV programs, namely commercials, basketball games, football games, news reports, and weather forecasts. Eight frame-based audio features are used to characterize the low-level audio properties,(More)
An emerging trend in the banking industry is to digitize checks for storage and transmission. An immediate requirement for eecient storage and transmission is check image compression. General purpose compression algorithms such as JPEG and wavelet-based methods produce annoying ringing or blocking artifacts at high compression ratios. In this paper, a(More)
Analysis and classification of the scene content of a video sequence are very important for content-based indexing and retrieval of multimedia databases. In this paper, we report our research on using the associated audio information for video scene classification. We describe several audio features that have been found effective in distinguishing audio(More)
Along with the advance in multimedia and internet technology , a h uge amount of data, including digital video and audio, are generated daily. Tools for eecient indexing and retrieval are indispensable. With multi-modal information present in the data, eeective i n tegration is necessary and is still a challenging problem. In this paper, we present four(More)
Video classiication and segmentation are fundamental steps for eecient accessing, retrieving and browsing large amount of video data. We h a ve developed a scene classiication scheme using a Hidden Markov Model HMM-based classiier. By utilizing the temporal behaviors of diierent scene classes, HMM classiier can eeectively classify video segments into one of(More)
This paper describes a technique for classifying TV broadcast video using Hidden Markov Model (HMM) [1]. Here we consider the problem of discriminating five types of TV programs, namely commercials, basketball games, football games, news reports, and weather forecasts. Eight frame-based audio features are used to characterize the low-level audio properties,(More)
A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The(More)