Wenguan Wang

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Unsupervised video object segmentation methods aim at automatically extracting the object from the whole video. Such segmentation has shown to benefit many specific visual tasks, such as video summarization and compression. Several methods [1, 3, 5] explored the notion of what a foreground object should look like in video data. These approaches generate(More)
We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities(More)
With ever-increasing volumes of video data, automatic extraction of salient object regions became even more significant for visual analytic solutions. This surge has also opened up opportunities for taking advantage of collective cues encapsulated in multiple videos in a cooperative manner. However, it also brings up major challenges, such as handling of(More)
We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the(More)
In this paper, we show that large annotated datasets datasets have great potential to provide strong priors for saliency estimation rather than merely serving for benchmark evaluations. To this end, we present a novel image saliency detection method called saliency transfer. Given an input image, we first retrieve a support set of best matches from the(More)
In this paper, we propose a real-time image superpixel segmentation method with 50fps by using the Density- Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with colorsimilarity(More)
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