Spatio-temporal grouping with constraint for seam carving in video summary application

Decombas, Marc; Dufaux, Frédéric; Pesquet‐Popescu, Béatrice

doi:10.1109/icdsp.2013.6622744

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…In [99] the authors used attention algorithms for video retargeting based on seam carving. An efficient spatiotemporal grouping is done to determine the temporal rate of reduction depending on the content, to suppress groups of isolated seams, to identify spatiotemporal groups of seams, and to approximate by constant segments the number of seams for each group while keeping the total sum of seams constant.…”

Section: Seam Carvingmentioning

confidence: 99%

Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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International audienceThis chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models. There are numerous applications and we try here to remain as exhaustive as possible to provide a picture of all the applications of saliency models, but also to detect where future developments might be of interest. The applications are grouped into three categories. The first one uses the detection of the most important regions in an image and contains applications such as video surveillance, audio surveillance, defect detection, pathology detection, expressive and social gestures, computer graphics and quality metrics. The second category uses saliency maps to detect the regions which are the less interesting in an image. Here one can found applications like texture metrics, compression, retargeting, summarization, watermarking and attention-based ad insertion. Finally, a third category uses the most interesting areas in an image with further processing like comparisons between those areas. In this category one can find image registration and landmarks, object recognition, action guidance in robotics or avatars, web sites optimization, images memorability, best viewpoint, symmetries and automatic focus on images

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Section: Seam Carvingmentioning

confidence: 99%

Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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“…Dynamic video synopsis is classified into paragraph splicing and object fitting. Paragraph splicing [28,29] splits spatial-temporal pixels into different video image areas from different time passages to create a short video as video synopsis. Object fitting [30,31] dynamically adapts moving object subgraphs to the video background to create a short video synopsis.…”

Section: Introductionmentioning

confidence: 99%

Surveillance Video Synopsis in GIS

Xie

Wang

Liu

et al. 2017

IJGI

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Surveillance videos contain a considerable amount of data, wherein interesting information to the user is sparsely distributed. Researchers construct video synopsis that contain key information extracted from a surveillance video for efficient browsing and analysis. Geospatial-temporal information of a surveillance video plays an important role in the efficient description of video content. Meanwhile, current approaches of video synopsis lack the introduction and analysis of geospatial-temporal information. Owing to the preceding problems mentioned, this paper proposes an approach called "surveillance video synopsis in GIS". Based on an integration model of video moving objects and GIS, the virtual visual field and the expression model of the moving object are constructed by spatially locating and clustering the trajectory of the moving object. The subgraphs of the moving object are reconstructed frame by frame in a virtual scene. Results show that the approach described in this paper comprehensively analyzed and created fusion expression patterns between video dynamic information and geospatial-temporal information in GIS and reduced the playback time of video content.

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“…Certain unique or not ordinary objects that stand out may draw human attention, whereas other less significant ones are the key to understanding the registered view. Regardless of these difficulties, the detection of salient objects has become a very important research field with potential applications in areas like object segmentation [2], video summarization [3], compression [4]- [6], image resizing [7] and image retrieval [8]. In the past two decades, many approaches to saliency detection have been proposed, but most focus on detecting the most important objects in a single image.…”

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confidence: 99%

CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework

2022

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In this paper, we propose a framework for evaluation of co-salient object detection algorithms along with two novel CoSOD methods. The processing pipeline of this framework is based on the CoEGNet algorithm, where the saliency detection part can be easily replaced by any saliency detector to be evaluated. By leveraging the proposed framework, we developed two new algorithms: one based on U2Net and the other based on the label decoupling framework (LDF). They are called in this paper CoU2Net and CoLDF, respectively. The proposed solutions were tested on three datasets: CoCA, CoSal2015, and CoSOD3k, and compared with some of the best algorithms in co-silent object detection: GICD and CoEGNet. The advantages and disadvantages of the proposed methods are highlighted and discussed. As a generalization of the aforementioned methods, a framework called Sal.Co has been also proposed. It is a modification of the CoEGNet method and it works on a saliency mask obtained from a saliency detector and attempts to indicate salient objects coexisting in a group of images. Both CoLDF and CoU2Net achieved better results than CoEGNet on the CoCA dataset. On the CoSal2015 and CoSOD3k datasets, they performed similarly to state-of-the-art methods, while maintaining a highly customizable structure. The source code can be found at: https://github.com/jakubkorczakowski/cosal_sal_testing. INDEX TERMS deep learning, image analysis, object detection, pattern recognition I. INTRODUCTION

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Spatio-temporal grouping with constraint for seam carving in video summary application

Cited by 6 publications

References 13 publications

Applications of Saliency Models

Applications of Saliency Models

Surveillance Video Synopsis in GIS

CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework

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