8,046 research outputs found
Deep Contrast Learning for Salient Object Detection
Salient object detection has recently witnessed substantial progress due to
powerful features extracted using deep convolutional neural networks (CNNs).
However, existing CNN-based methods operate at the patch level instead of the
pixel level. Resulting saliency maps are typically blurry, especially near the
boundary of salient objects. Furthermore, image patches are treated as
independent samples even when they are overlapping, giving rise to significant
redundancy in computation and storage. In this CVPR 2016 paper, we propose an
end-to-end deep contrast network to overcome the aforementioned limitations.
Our deep network consists of two complementary components, a pixel-level fully
convolutional stream and a segment-wise spatial pooling stream. The first
stream directly produces a saliency map with pixel-level accuracy from an input
image. The second stream extracts segment-wise features very efficiently, and
better models saliency discontinuities along object boundaries. Finally, a
fully connected CRF model can be optionally incorporated to improve spatial
coherence and contour localization in the fused result from these two streams.
Experimental results demonstrate that our deep model significantly improves the
state of the art.Comment: To appear in CVPR 201
Image Co-saliency Detection and Co-segmentation from The Perspective of Commonalities
University of Technology Sydney. Faculty of Engineering and Information Technology.Image co-saliency detection and image co-segmentation aim to identify the common salient objects and extract them in a group of images.
Image co-saliency detection and image co-segmentation are important for many content-based applications such as image retrieval, image editing, and content aware image/video compression. The image co-saliency detection and image co-segmentation are very close works. The most important part in these two works is the definition of the commonality of the common objects. Usually, common objects share similar low-level features, such as appearances, including colours, textures shapes, etc. as well as the high-level semantic features.
In this thesis, we explore the commonalities of the common objects in a group of images from low-level features and high-level features, and the way to achieve the commonalities and finally segment the common objects. Three main works are introduced, including an image co-saliency detection model and two image co-segmentation methods.
, an image co-saliency detection model based on region-level fusion and pixel-level refinement is proposed. The commonalities between the common objects are defined by the appearance similarities on the regions from all the images. It discovers the regions that are salient in each individual image as well as salient in the whole image group. Extensive experiments on two benchmark datasets demonstrate that the proposed co-saliency model consistently outperforms the state-of-the-art co-saliency models in both subjective and objective evaluation.
, an unsupervised images co-segmentation method via guidance of simple images is proposed. The commonalities are still defined by hand-crafted features on regions, colours and textures, but not calculated among regions from all the images. It takes advantages of the reliability of simple images, and successfully improves the performance. The experiments on the dataset demonstrate the outperformance and robustness of the proposed method.
, a learned image co-segmentation model based on convolutional neural network with multi-scale feature fusion is proposed. The commonalities between objects are not defined by handcraft features but learned from the training data. When training a neural network with multiple input images simultaneously, the resource cost will increase rapidly with the inputs. To reduce the resource cost, reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-the-art methods while the network has successfully gotten simplified and the resources cost is reduced
Hierarchical Salient Object Detection for Assisted Grasping
Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.Comment: Accepted for ICRA 201
The Secrets of Salient Object Segmentation
In this paper we provide an extensive evaluation of fixation prediction and
salient object segmentation algorithms as well as statistics of major datasets.
Our analysis identifies serious design flaws of existing salient object
benchmarks, called the dataset design bias, by over emphasizing the
stereotypical concepts of saliency. The dataset design bias does not only
create the discomforting disconnection between fixations and salient object
segmentation, but also misleads the algorithm designing. Based on our analysis,
we propose a new high quality dataset that offers both fixation and salient
object segmentation ground-truth. With fixations and salient object being
presented simultaneously, we are able to bridge the gap between fixations and
salient objects, and propose a novel method for salient object segmentation.
Finally, we report significant benchmark progress on three existing datasets of
segmenting salient objectsComment: 15 pages, 8 figures. Conference version was accepted by CVPR 201
On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection
It has become apparent that a Gaussian center bias can serve as an important
prior for visual saliency detection, which has been demonstrated for predicting
human eye fixations and salient object detection. Tseng et al. have shown that
the photographer's tendency to place interesting objects in the center is a
likely cause for the center bias of eye fixations. We investigate the influence
of the photographer's center bias on salient object detection, extending our
previous work. We show that the centroid locations of salient objects in
photographs of Achanta and Liu's data set in fact correlate strongly with a
Gaussian model. This is an important insight, because it provides an empirical
motivation and justification for the integration of such a center bias in
salient object detection algorithms and helps to understand why Gaussian models
are so effective. To assess the influence of the center bias on salient object
detection, we integrate an explicit Gaussian center bias model into two
state-of-the-art salient object detection algorithms. This way, first, we
quantify the influence of the Gaussian center bias on pixel- and segment-based
salient object detection. Second, we improve the performance in terms of F1
score, Fb score, area under the recall-precision curve, area under the receiver
operating characteristic curve, and hit-rate on the well-known data set by
Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we
exemplarily demonstrate that implicit center biases are partially responsible
for the outstanding performance of state-of-the-art algorithms. Last but not
least, as a result of debiasing Cheng et al.'s algorithm, we introduce a
non-biased salient object detection method, which is of interest for
applications in which the image data is not likely to have a photographer's
center bias (e.g., image data of surveillance cameras or autonomous robots)
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