252 research outputs found
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
Crowd Saliency Detection via Global Similarity Structure
It is common for CCTV operators to overlook inter- esting events taking place
within the crowd due to large number of people in the crowded scene (i.e.
marathon, rally). Thus, there is a dire need to automate the detection of
salient crowd regions acquiring immediate attention for a more effective and
proactive surveillance. This paper proposes a novel framework to identify and
localize salient regions in a crowd scene, by transforming low-level features
extracted from crowd motion field into a global similarity structure. The
global similarity structure representation allows the discovery of the
intrinsic manifold of the motion dynamics, which could not be captured by the
low-level representation. Ranking is then performed on the global similarity
structure to identify a set of extrema. The proposed approach is unsupervised
so learning stage is eliminated. Experimental results on public datasets
demonstrates the effectiveness of exploiting such extrema in identifying
salient regions in various crowd scenarios that exhibit crowding, local
irregular motion, and unique motion areas such as sources and sinks.Comment: Accepted in ICPR 2014 (Oral). Mei Kuan Lim and Ven Jyn Kok share
equal contribution
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