10,729 research outputs found
An End-to-End Network for Co-Saliency Detection in One Single Image
As a common visual problem, co-saliency detection within a single image does
not attract enough attention and yet has not been well addressed. Existing
methods often follow a bottom-up strategy to infer co-saliency in an image,
where salient regions are firstly detected using visual primitives such as
color and shape, and then grouped and merged into a co-saliency map. However,
co-saliency is intrinsically perceived in a complex manner with bottom-up and
top-down strategies combined in human vision. To deal with this problem, a
novel end-to-end trainable network is proposed in this paper, which includes a
backbone net and two branch nets. The backbone net uses ground-truth masks as
top-down guidance for saliency prediction, while the two branch nets construct
triplet proposals for feature organization and clustering, which drives the
network to be sensitive to co-salient regions in a bottom-up way. To evaluate
the proposed method, we construct a new dataset of 2,019 nature images with
co-saliency in each image. Experimental results show that the proposed method
achieves a state-of-the-art accuracy with a running speed of 28fps
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.Comment: 14 pages, 7 figure
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