Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.

1. Automatic Image Co-segmentation
Using Geometric Mean Saliency
-IEEE ICIP 2014
Koteswar Rao Jerripothula, Jianfei Cai, Fanman Meng, and Junsong Yuan, “Automatic image Co-Segmentation using geometric
mean saliency,” in 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, Oct. 2014, pp. 3282–3286

2. Introduction
Give more images containing the same object. Now goal becomes to extract
the common object. This is co-segmentation.
The task of jointly segmenting out the shared object in a given set of similar
images is known as Image Cosegmentation.

3. Challenges
• Co-segmentation may not always perform
better than single-image segmentation.
• Complicated co-labeling and large number of
parameters make co-segmentation difficult
with increasing diversity.

4. Idea: Images containing salient common objects can help images containing weakly
salient common objects
Weakly salient common object images

6. Proposed Method
1) Saliency Enhancement: Local contrast based saliency is added to global
contrast based saliency map and is brightened to avoid over penalty in step 4.
2) Subgroup Formation: Enhanced saliency maps are used as weights for weighted
GIST descriptor which is used for clustering the images by k-means algorithm.
3) Pixel correspondence: Enhanced saliency maps are used as masks for masked
SIFT dense correspondence to develop warped saliency maps
4) Geometric Mean Saliency: Geometric mean function is used to fuse the main
saliency map and all the warped saliency maps.
5) Image Segmentation: Resultant GMS map is first regularized at super-pixel
level and then foreground and background seeds are selected from it for Grab
Cut segmentation.

8. Formulation
1 2 1 2Let { , ,..., }and { , ,..., } be set of images and
corresponding enhanced saliency maps in a sub-group respectively.
is warped saliency map of for such that ( ) ( ')
where '
n n
j j
i j i i j
I I I I M M M M n
U I I U p M p
p
 

{1,.., }
is the corresponding pixel in for pixel in
( ) ( ) ( )
, if ( )
, if ( )
where is a parameter and is global threshold value of .
and
j i
j n
j
n
i i i
j i
i i
i i
i
i i
I p I
GMS p M p U p
F GMS p
p
B GMS p
GMS
F B


 







are foreground and background seeds.

9. Results

11. References:
[Distributed] G.Kim,E. Xing, L. Fei-Fei, and T.Kanade. Distributed cosegmentation via submodular optimization on anisotropic diffusion. ICCV 2011.
[Discriminative] A. Joulin, F.Bach, and J. Ponce. Discriminative clustering for image cosegmentation. CVPR 2010
[Multi-class] A. Joulin, F.Bach, and J. Ponce. Multi-class cosegmentation. CVPR 2012
[Object Discovery] M. Rubinstein, A. Joulin, J. Kopf, and C. Liu. Unsupervsed joint object discovery and segmentation in internet images. CVPR 2013.
[Cosketch] J. Dai, Y. Wu, J. Zhou, and S. Zhu. Cosegmentation and cosketch by unsupervised learning. ICCV 2013