Content-driven adaptation of on-line video

Bescós, Jesús; Martínez, José María Álvarez; Herranz, Luis; Tiburzi, Fabricio

doi:10.1016/j.image.2007.05.009

Cited by 12 publications

(9 citation statements)

References 34 publications

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“…This was addressed in [11] for 2D video segmentation, where it was demonstrated that such evaluation is mostly subjective. Since 3D perception is essentially a brain-driven process, the subjective nature of such evaluation is even more pronounced in this type of media.…”

Section: Introductionmentioning

confidence: 99%

3D video shot boundary detection based on clustering of depth-temporal features

Ferreira

Assunção

Cruz

2013

2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)

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This paper proposes an algorithm for automatic detection of 3D video shots with different perceptual features. The proposed algorithm is able to identify distinct three-dimensional visual scenes by detecting 3D video shot boundaries based on clustering of depth-temporal features. A combination of texture variation along the temporal dimension and depth variance is used by K-means clustering to find the stereo frames which comprised the 3D scene boundaries. An important characteristic of the proposed algorithm in comparison with others published in the literature for temporal segmentation of classic 2D video is that no thresholds are used in the decision processes neither training data sets. The experimental results show that the proposed method is capable of achieving high recall (e.g., 0.95) and precision rate (e.g., 1.0) in video sequences with both sharp and smooth 3D scene transitions.Index Terms-3D video segmentation, shot boundary detection, recall and precision rate.

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Section: Introductionmentioning

confidence: 99%

3D video shot boundary detection based on clustering of depth-temporal features

Ferreira

Assunção

Cruz

2013

2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)

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“…The problem of scalable coding of video summary has been recently addressed in [4], [5] and [6]. In [4] the authors propose a hierarchical frame selection scheme which considers semantic relevance in video sequences at different levels from compressed wavelet-based scalable video.…”

Section: Introductionmentioning

confidence: 99%

Efficient scalable coding of video summaries using dynamic GOP structures

Ferreira

Cruz

Assunção

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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A method to efficiently encode an arbitrary video summary with temporal scalability and dynamic Group of Pictures (GOP) structures is proposed in this paper. The video summary is encoded as the base layer of a Scalable Video Coding (SVC) bitstream using a novel approach, which matches the summary frames available in temporal segments onto corresponding dynamic GOP structures. An algorithm is devised to compute variable GOP sizes along with an efficient independent prediction structure for the summary. The results show that the proposed method can be used to encode arbitrary video summaries with increased efficiency in the temporal base layer and negligible loss of R-D performance in the whole scalable sequence.

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“…However, it is found that objective defining of COI is difficult due to the fact that COIs are inevitably dependent on both the application domain and the users' preferences. One of the most widely investigated application is sports video summarization [10,13,14,25,34], followed by movies [5,7,21,30,35], news [18,32], surveillance [6], home videos [28] and web videos [19,24]. Regarding the users' preferences, personalized and user-adaptive summarization techniques are introduced and can be found in [10,11,28].…”

Section: Introductionmentioning

confidence: 99%

Activity-driven content adaptation for effective video summarization

Ren

Jiang

Feng

2010

Journal of Visual Communication and Image Representation

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a b s t r a c tIn this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided.

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Content-driven adaptation of on-line video

Cited by 12 publications

References 34 publications

3D video shot boundary detection based on clustering of depth-temporal features

3D video shot boundary detection based on clustering of depth-temporal features

Efficient scalable coding of video summaries using dynamic GOP structures

Activity-driven content adaptation for effective video summarization

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