9,922 research outputs found
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
We provide a comprehensive evaluation of salient object detection (SOD)
models. Our analysis identifies a serious design bias of existing SOD datasets
which assumes that each image contains at least one clearly outstanding salient
object in low clutter. The design bias has led to a saturated high performance
for state-of-the-art SOD models when evaluated on existing datasets. The
models, however, still perform far from being satisfactory when applied to
real-world daily scenes. Based on our analyses, we first identify 7 crucial
aspects that a comprehensive and balanced dataset should fulfill. Then, we
propose a new high quality dataset and update the previous saliency benchmark.
Specifically, our SOC (Salient Objects in Clutter) dataset, includes images
with salient and non-salient objects from daily object categories. Beyond
object category annotations, each salient image is accompanied by attributes
that reflect common challenges in real-world scenes. Finally, we report
attribute-based performance assessment on our dataset.Comment: ECCV 201
Deep Saliency with Encoded Low level Distance Map and High Level Features
Recent advances in saliency detection have utilized deep learning to obtain
high level features to detect salient regions in a scene. These advances have
demonstrated superior results over previous works that utilize hand-crafted low
level features for saliency detection. In this paper, we demonstrate that
hand-crafted features can provide complementary information to enhance
performance of saliency detection that utilizes only high level features. Our
method utilizes both high level and low level features for saliency detection
under a unified deep learning framework. The high level features are extracted
using the VGG-net, and the low level features are compared with other parts of
an image to form a low level distance map. The low level distance map is then
encoded using a convolutional neural network(CNN) with multiple 1X1
convolutional and ReLU layers. We concatenate the encoded low level distance
map and the high level features, and connect them to a fully connected neural
network classifier to evaluate the saliency of a query region. Our experiments
show that our method can further improve the performance of state-of-the-art
deep learning-based saliency detection methods.Comment: Accepted by IEEE Conference on Computer Vision and Pattern
Recognition(CVPR) 2016. Project page:
https://github.com/gylee1103/SaliencyEL
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