2,112 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
Salient Object Detection via Structured Matrix Decomposition
Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank
matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First,
previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations
of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the
salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To
address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a
tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have
similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature
space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model
for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show
competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics
Visual Saliency Based on Multiscale Deep Features
Visual saliency is a fundamental problem in both cognitive and computational
sciences, including computer vision. In this CVPR 2015 paper, we discover that
a high-quality visual saliency model can be trained with multiscale features
extracted using a popular deep learning architecture, convolutional neural
networks (CNNs), which have had many successes in visual recognition tasks. For
learning such saliency models, we introduce a neural network architecture,
which has fully connected layers on top of CNNs responsible for extracting
features at three different scales. We then propose a refinement method to
enhance the spatial coherence of our saliency results. Finally, aggregating
multiple saliency maps computed for different levels of image segmentation can
further boost the performance, yielding saliency maps better than those
generated from a single segmentation. To promote further research and
evaluation of visual saliency models, we also construct a new large database of
4447 challenging images and their pixelwise saliency annotation. Experimental
results demonstrate that our proposed method is capable of achieving
state-of-the-art performance on all public benchmarks, improving the F-Measure
by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset
(HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively
on these two datasets.Comment: To appear in CVPR 201
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