1,683 research outputs found
Salient Object Detection via Augmented Hypotheses
In this paper, we propose using \textit{augmented hypotheses} which consider
objectness, foreground and compactness for salient object detection. Our
algorithm consists of four basic steps. First, our method generates the
objectness map via objectness hypotheses. Based on the objectness map, we
estimate the foreground margin and compute the corresponding foreground map
which prefers the foreground objects. From the objectness map and the
foreground map, the compactness map is formed to favor the compact objects. We
then derive a saliency measure that produces a pixel-accurate saliency map
which uniformly covers the objects of interest and consistently separates fore-
and background. We finally evaluate the proposed framework on two challenging
datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that
our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape
Unconstrained salient object detection via proposal subset optimization
We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that directly outputs a compact set of detection windows, if any, for an input image. Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects. Location proposals tend to be highly overlapping and noisy. Based on the Maximum a Posteriori principle, we propose a novel subset optimization framework to generate a compact set of detection windows out of noisy proposals. In experiments, we show that our subset optimization formulation greatly enhances the performance of our system, and our system attains 16-34% relative improvement in Average Precision compared with the state-of-the-art on three challenging salient object datasets.http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Unconstrained_Salient_Object_CVPR_2016_paper.htmlPublished versio
A Reverse Hierarchy Model for Predicting Eye Fixations
A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.Comment: CVPR 2014, 27th IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). CVPR 201
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