Video clustering using spatio-temporal image with fixed length

Okamoto, Hiroshi; Yasugi, Yukinobu; Babaguchi, Noboru; Kitahashi, Tadahiro

doi:10.1109/icme.2002.1035716

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Ngo et al [5] integrated both tensor and color histograms to carry out a two-level hierarchical clustering of shots based on temporal slices. Likewise, Okamoto et al [6] used spatio-temporal image slices of fixed length for clustering. Moreover, some advanced statistical models have been proposed to detect scenes.…”

Section: Scene Segmentationmentioning

confidence: 99%

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

Wang

Duan

Liu

et al. 2008

IEEE Trans. Multimedia

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With the advance of digital video recording and playback systems, the request for efficiently managing recorded TV video programs is evident so that users can readily locate and browse their favorite programs. In this paper, we propose a multimodal scheme to segment and represent TV video streams. The scheme aims to recover the temporal and structural characteristics of TV programs with visual, auditory, and textual information. In terms of visual cues, we develop a novel concept named program-oriented informative images (POIM) to identify the candidate points correlated with the boundaries of individual programs. For audio cues, a multiscale Kullback-Leibler (K-L) distance is proposed to locate audio scene changes (ASC), and accordingly ASC is aligned with video scene changes to represent candidate boundaries of programs. In addition, latent semantic analysis (LSA) is adopted to calculate the textual content similarity (TCS) between shots to model the inter-program similarity and intra-program dissimilarity in terms of speech content. Finally, we fuse the multimodal features of POIM, ASC, and TCS to detect the boundaries of programs including individual commercials (spots). Towards effective program guide and attracting content browsing, we propose a multimodal representation of individual programs by using POIM images, key frames, and textual keywords in a summarization manner. Extensive experiments are carried out over an open benchmarking dataset TRECVID 2005corpus and promising results have been achieved. Compared with the electronic program guide (EPG), our solution provides a more generic approach to determine the exact boundaries of diverse TV programs even including dramatic spots.Index Terms-Broadcast video, latent semantic analysis, multimodal fusion, TV program segmentation.

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Section: Scene Segmentationmentioning

confidence: 99%

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

Wang

Duan

Liu

et al. 2008

IEEE Trans. Multimedia

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“…Indicatively, Ristivojević et al have used the volumetric representation for three-dimensional (3D) segmentation, where the notion of ''object tunnel'' is used to describe the volume carved out by a moving object in this volume [36]. Okamoto et al used a similar volumetric framework for video clustering, where video shots are selected based on their spatiotemporal texture homogeneity [17].…”

Section: Introductionmentioning

confidence: 99%

“…The concept of saliency detectors operating in spatiotemporal neighborhoods has only recently begun to be used for spatiotemporal analysis with emerging applications to video classification [17,26], event detection [20,32,39,49] and activity recognition [20,33].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal saliency for video classification

Rapantzikos

Tsapatsoulis

Avrithis

et al. 2009

Signal Processing: Image Communication

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“…Indicatively, Ristivojević et al have used the volumetric representation for 3D segmentation, where the notion of "object tunnel" is used to describe the volume carved out by a moving object in this volume (Ristivojević and Konrad 2006). Okamoto et al used a similar volumetric framework for video clustering, where video shots are selected based on their spatiotemporal texture homogeneity (Okamoto et al 2002). Quite recently, Mahadevan and Vasconcelos (2009) proposed a biologically plausible model for spatiotemporal saliency.…”

Section: Volumetric Saliency By Feature Competitionmentioning

confidence: 99%

Vision, Attention Control, and Goals Creation System

Rapantzikos

Avrithis²,

Kolias³

2010

Perception-Action Cycle

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Biological visual attention has been long studied by experts in the field of cognitive psychology. The Holy Grail of this study is the exact modeling of the interaction between the visual sensory and the process of perception. It seems that there is an informal agreement on the four important functions of the attention process: (a) the bottom-up process, which is responsible for the saliency of the input stimuli; (b) the top-down process that bias attention toward known areas or regions of predefined characteristics; (c) the attentional selection that fuses information derived from the two previous processes and enables focus; and (d) the dynamic evolution of the attentional selection process. In the following, we will outline established computational solutions for each of the four functions. OverviewMost of our impressions and memories are based on vision. Nevertheless vision mechanisms and functionalities are still not apparent. How do we perceive shape, color, or motion and how do we automatically focus on the most informative parts of the visual input? It has been long established that primates, including human, use focused attention and fast saccades to analyze visual stimuli based on the current situation or the desired goal. Neuroscientists have proven that neural information related to shape, motion, and color is transmitted through, at least, three parallel and interconnected channels to the brain rather than a single one. Hence a second question arises related to how these channels are "linked" in order to provide useful information to the brain.The Human Visual System (HVS) creates a perceptual representation of the world that is quite different than the two dimensional depiction of the retina.K. Rapantzikos ( ) Image, Video and

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Video clustering using spatio-temporal image with fixed length

Cited by 11 publications

References 6 publications

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

Spatiotemporal saliency for video classification

Vision, Attention Control, and Goals Creation System

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