A context-based approach to crop and scale video for broadcast applications

Deigmoeller, Joerg; Itagaki, Takebumi; Stoll, Gerhard; Just, Norbert

doi:10.1109/isbmsb.2010.5463077

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2014

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“…Visual attention information can benefit many applications, such as image retrieval [9,29,5], automatic image cropping [26,8,23], adaptive compression of images [7] and object segmentation [15,10].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Sampling based Salient Region Detection

Zhang

Jin

et al. 2014

Proceedings of International Conference on Multimedia Retrieval

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In this paper, a simple and e↵ective method is proposed for salient region detection. Based on the observation that salient regions tend to be compact, connected and surrounded, our original idea is to exploit these three kinds of prior knowledge. However, concepts of spatial structure (such as connectivity and surroundedness) only have definite meanings in binary images. Thus, a Monte Carlo Sampling based Saliency model is proposed. Our model has two main advantages over other methods. Firstly, the result of each sampling process is a binary map which can greatly simplify the combination with prior knowledge of spatial structure. Secondly, our method is naturally parallelized because every sampling process is independent with each other, which makes our method very e cient. Experimental results on two datasets show that, compared with eleven state-of-theart methods, our approach has a competitive performance and also runs very fast.

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