A Video Browsing enabled Personal Video Recorder

Otsuka, I.; Shipman, Sam; Divakaran, Ajay

doi:10.1007/978-0-387-76569-3_14

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…In [14], highlights were extracted from baseball programs and in [2] an adaptive approach to sports video highlight detection was proposed and studied in detail for the case of soccer. The usefulness of such affective video indexing techniques is witnessed by the fact that Mitsubishi has already released two products taking advantage of the highlight detection for sport events in Japan [15]. Retrieval of movie clips using multimedia content features and user-assigned keywords was investigated by [3] and [16].…”

Section: The Contribution Of Corpus Developmentmentioning

confidence: 99%

Corpus Development for Affective Video Indexing

Soleymani

Larson

Pun

et al. 2014

IEEE Trans. Multimedia

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Affective video indexing is the area of research that develops techniques to automatically generate descriptions of video content that encode the emotional reactions which the video content evokes in viewers. This paper provides a set of corpus development guidelines based on state-of-the-art practice intended to support researchers in this field. Affective descriptions can be used for video search and browsing systems offering users affective perspectives. The paper is motivated by the observation that affective video indexing has yet to fully profit from the standard corpora (data sets) that have benefited conventional forms of video indexing. Affective video indexing faces unique challenges, since viewer-reported affective reactions are difficult to assess. Moreover affect assessment efforts must be carefully designed in order to both cover the types of affective responses that video content evokes in viewers and also capture the stable and consistent aspects of these responses. We first present background information on affect and multimedia and related work on affective multimedia indexing, including existing corpora. Three dimensions emerge as critical for affective video corpora, and form the basis for our proposed guidelines: the context of viewer response, personal variation among viewers, and the effectiveness and efficiency of corpus creation. Finally, we present examples of three recent corpora and discuss how these corpora make progressive steps towards fulfilling the guidelines

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Section: The Contribution Of Corpus Developmentmentioning

confidence: 99%

Corpus Development for Affective Video Indexing

Soleymani

Larson

Pun

et al. 2014

IEEE Trans. Multimedia

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“…For example, based on results in [28], Otsuka et al [26] developed a personal video recorder that detected highlights in sports videos via recognition of a small number of sound classes, specific to sport context, the most important being a mixture of the excited commentator's speech with audience cheering. This personal video recorder successfully worked in the Japanese market for several years.…”

Section: Related Workmentioning

confidence: 99%

“…Systems for detecting event highlights are often tailored to a particular context, too: for example, interesting moments of a sport programme in [26] are detected via recognition of a mixture of audience cheering with an excited commentator speech, being a specific to sports context sound class. On the other hand, systems that do not rely on recognition of pre-defined context-specific cues usually require computationally expensive re-training, as e.g.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised context adaptation: case study of audience excitement recognition

et al. 2012

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To recognise just the same human reaction (for example, a strong excitement) in different contexts, customary behaviours in these contexts have to be taken into account; e.g. a happy sport audience may be cheering for long time, while a happy theatrical audience may produce only short bursts of laughter in order to not interrupt the performance. Tailoring recognition algorithms to contexts can be achieved by building either a context-specific or a generic system. The former is individually trained for each context to recognise sets of characteristic responses, whereas the latter-in contrast to the context-specific one-adapts to the context via significantly more lightweight modification of parameters. This paper follows the latter way and proposes a simple modification of a hidden Markov model (HMM) classifier that enables end users to adapt the generic system to a context or a personal perception of an annotator by labelling a fairly small number of data samples of each context. For better adaptability to the limited number of the user's annotations, the proposed semisupervised HMM classifier employs the maximum posterior marginal, rather than the more conventional maximum a posteriori decision rule. The proposed user-and contextadaptable semi-supervised HMM classifier was tested on recognising excitement of a show audience in three contexts (a concert hall, a circus, and a sport event), differing in how the excitement is expressed. In our experiments the proposed classifier recognised reactions of a non-neutral audience with 10% higher accuracy than the conventional HMM and support vector machine based classifiers.

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“…Divakaran and Otsuka [40] argued that "Current personal video recorders can store hundreds of hours of content and the future promises even greater storage capacity. Manual navigation through such large volumes of content would be tedious if not infeasible."…”

Section: Video Browsing Applications Using Video-player-like Interactionmentioning

confidence: 99%

Video browsing interfaces and applications: a review

et al. 2010

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Abstract. We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data-which, if presented in its raw format, is rather unwieldy and costly-have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-playerlike interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other. C 2010 Society of Photo-Optical Instrumentation Engineers.

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A Video Browsing enabled Personal Video Recorder

Cited by 3 publications

References 7 publications

Corpus Development for Affective Video Indexing

Corpus Development for Affective Video Indexing

Semi-supervised context adaptation: case study of audience excitement recognition

Video browsing interfaces and applications: a review

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