A generic framework of user attention model and its application in video summarization

By Yufei Ma, Xiansheng Hua, Lie Lu, HongJiang Zhang. IEEE Trans. Multim., 2005

551 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4541e6605c73c2499d145fb3c8621b91fddf3a78

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In Computer Vision and Pattern Recognition

Citations in the corpus

Perceptual visual quality metrics: A survey (2011, background): “[95], aural attention was also considered and integrated with visual factors.”
State-of-the-Art in Visual Attention Modeling (2013, unknown): “[33] proposed a user attention model to video contents by incorporating top-down factors into the classical bottom-up framework by extracting semantic cues (e.”
Multi-View Video Summarization (2010, methodology): “The features in other modalities, such as textual and aural features used in previous video analysis methods [11], [19] , however, can also be integrated into our method.”
Attentive Systems: A Survey (2017, methodology): “Ma et al. (2005) propose a feasible solution for video summarization, including key-frame selection and video skim extraction, based on user attention model, which does not require sophisticated heuristic rules or full semantic understanding.”
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection (2020, background): “It has many useful applications, including object segmentation and recognition [27, 32, 39, 51, 70, 79], image/video compression [24], video detection/summarization [19, 41], content-based image editing [14, 23, 42, 57, 63], informative common object discovery [71,72], and image retrieval [8,22,37].”
What Makes a Patch Distinct? (2013, background): “The detection of the most salient region of an image has numerous applications, including object detection and recognition [13], image compression [10], video summarization [16], and photo collage [8], to name a few.”
A Survey on Visual Content-Based Video Indexing and Retrieval (2011, background): “New video retrieval schemes could be based on the detection and extraction of image regions or video behaviors that attract users’ attention [46], [162].”
Video summarisation: A conceptual framework and survey of the state of the art (2008, methodology): “[62] who managed to produce perception based summaries through the use of applied theories during analysis.”
Salient Object Detection: A Benchmark (2015, background): “compression [31], [32], video summarization [33]–[35], photo collage/media re-targeting/cropping/thumb-nailing [20],”
Category-Specific Video Summarization (2014, unknown): “Without supervision, summarization methods must rely on low-level indices to determine the relevance of parts of a video [9,11,12].”
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, background): “, object recognition [24][8][83], object detection [21][4], target tracking [9], image compression [10], and video summarization [7]); computer graphics (e.”