object saliency
TRANSCRIPT
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Object co-segmentation acrossmultiple images using saliency
Abhijit SharangMohd. Dawood
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Introduction
Co-segmentation aims to segment common objects from a collection of
images given by the user.
Compared with traditional segmentation methods, co-segmentation can
accurately segment common objects from images by several relatedimages.
The task is less cumbersome and requires lesser supervision.
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Past work
Most existing co-segmentation methods take foreground similarity under
consideration
[Rother et.al,06],[Singh et.al,09] and [Hochbaum et al.,09] model co-
segmentation as an optimisation problem with added constraints on
foreground similarity.
Special clustering and discriminative clustering were combined by [Joulin
et al.,10] to perform co-segmentation
In order to segment common objects,[Vicente et al.,11] selected useful
features from a total of 33 features through random forest regressor.
[Kim et al.,11]proposed a diffusion-based optimization framework whichused anisotropic heat diffusion method to locate seeded points of the
common objects and employed random walks segmentation method for
common objects segmentation.
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Motivation for using saliency
The existing methods are based on similarity of features.
What if the background across the images also match?
Saliency becomes useful under such a case as low score is attached to
background
Moreover, the saliency of the common object can be boosted by taking
into account the saliency of similar objects across the images.
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The objective and methodology
Two fold objective: improving the saliency map by using information fromother images and using the improved saliency map for further
segmentation
Normal saliency map is constructed using the HC method discussed by
[Cheng et al.,11].
This map is used to construct the co-saliency map, where the co-saliencyof a colour kin image I is defined as:
ik = ik + ik ,
where,
ik = ( (
=
=1
=
=1 ,) ),m=number of images except the current image
p=number of colours in thejthimage
d(Cik ,Cjl) is the distance between the colours (i,k) and ( j,l)
jl
is the HC saliency value of the colour (j,l)
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Saliency comparison HC Saliency
Co-saliency
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Methodology(cont.)
Having obtained the co-saliency map for the images, we divide the images
into regions.
We then compute the region-wise saliency using the saliency of the
colours contained in the image, assigning higher weight to salient
colours.
regions
-----------------------
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Methodology(cont..) The salient object in the image might be fragmented into several
regions.
We perform region merging based on the idea that regions having
similar ratio of the salient pixels and normal pixels have a high
probability of belonging to the same object.
The saliency of the merged region thus obtained is so high that we can
perform a greedy selection of the region as the most salient region in the
image
Segmented Object
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Subjective results
Windmill (ICoseg dataset)
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Subjective results(cont..)
Pyramids(ICoseg dataset)
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Subjective results(cont..)Statue of Liberty(ICoseg dataset)
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Subjective results(cont..)
Kendo(ICoseg dataset)
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Objective results
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To do list..
Improving the region merging algorithm
Computing F-scores and Precision-Recall curves Testing the algorithm on more categories from ICoseg dataset and MSRC
dataset
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References Meng, F.; Li, H.; Liu, G.; Ngan, K. N.; , "Object Co-Segmentation Based on Shortest Path Algorithm
and Saliency Model," Multimedia, IEEE Transactions on , vol.14, no.5, pp.1429-1441, Oct. 2012
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