Video retargeting with nonlinear spatial-temporal saliency fusion

Lu, Taoran; Yuan, Zheng; Huang, Yu; Wu, Dapeng; Yu, Heather

doi:10.1109/icip.2010.5651644

Cited by 27 publications

(34 citation statements)

References 14 publications

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“…It is a useful concept for humans in their daily life and it holds an important place in computer vision applications such as object detection [1], image segmentation [2], robots navigation and localization [3], object tracking [4], image retargeting [5] and image/video compression [6].…”

Section: Introductionmentioning

confidence: 99%

On spatio-temporal saliency detection in videos using multilinear PCA

Sidibé

Rastgoo

Mériaudeau

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Section: Introductionmentioning

confidence: 99%

On spatio-temporal saliency detection in videos using multilinear PCA

Sidibé

Rastgoo

Mériaudeau

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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“…It is an important and fundamental research problem in neuroscience and psychology to investigate the mechanism of human visual system in selecting regions of interest in complex scenes. It has been an active research topic in computer vision research due to various applications such as object detection [1], image segmentation [2], robotic navigation and localization [3], video surveillance [4], object tracking [5], image re-targeting [6] and image/video compression [7].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Saliency Detection in Dynamic Scenes Using Local Binary Patterns

Muddamsetty

Sidibé

Trémeau

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Visual saliency detection is an important step in many computer vision applications, since it reduces further processing steps to regions of interest. Saliency detection in still images is a well-studied topic. However, videos scenes contain more information than static images, and this additional temporal information is an important aspect of human perception. Therefore, it is necessary to include motion information in order to obtain spatio-temporal saliency map for a dynamic scene. In this paper, we introduce a new spatio-temporal saliency detection method for dynamic scenes based on dynamic textures computed with local binary patterns. In particular, we extract local binary patterns descriptors in two orthogonal planes (LBP-TOP) to describe temporal information, and color features are used to represent spatial information. The obtained three maps are finally fused into a spatio-temporal saliency map. The algorithm is evaluated on a dataset with complex dynamic scenes and the results show that our proposed method outperforms state-of-art methods.

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“…Binary thresholded fusion [6]: first, a binary mask M B is generated by thresholding the static saliency map (the mean value of M S is used as threshold). The binary mask is used to exclude spatiotemporal inconsistent areas and to enhance the robustness of the final saliency map when the global motion parameters are not estimated properly.…”

Section: Fusion Techniquesmentioning

confidence: 99%

“…It has been applied to different application domains including object detection [1], predicting human eye fixations [2], segmentation [3], image/video compression [4], video surveillance [5], image retargeting [6] and mobile robot navigation [7].…”

Section: Introductionmentioning

confidence: 99%

A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes

Muddamsetty

Sidibé

Trémeau

et al. 2013

2013 IEEE International Conference on Image Processing

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Visual saliency is an important research topic in computer vision applications, which helps to focus on regions of interest instead of processing the whole image. Detecting visual saliency in still images has been widely addressed in literature. However, visual saliency detection in videos is more complicated due to additional temporal information. A spatio-temporal saliency map is usually obtained by the fusion of a static saliency map and a dynamic saliency map. The way both maps are fused plays a critical role in the accuracy of the spatio-temporal saliency map. In this paper, we evaluate the performances of different fusion techniques on a large and diverse dataset and the results show that a fusion method must be selected depending on the characteristics, in terms of color and motion contrasts, of a sequence. Overall, fusion techniques which take the best of each saliency map (static and dynamic) in the final spatio-temporal map achieve best results.

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Video retargeting with nonlinear spatial-temporal saliency fusion

Cited by 27 publications

References 14 publications

On spatio-temporal saliency detection in videos using multilinear PCA

On spatio-temporal saliency detection in videos using multilinear PCA

Spatio-temporal Saliency Detection in Dynamic Scenes Using Local Binary Patterns

A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes

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