Co-Segmentation. Presented By : Murad Tukan. Introduction. Today there is a massive attempt to exclude the same object from different images. Such problem is not an easy task as it seems , furthermore the algorithm which is presented today is not 100% accurate even though it is efficient.
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Presented By : Murad Tukan
Today there is a massive attempt to exclude the same object from different images.
Such problem is not an easy task as it seems , furthermore the algorithm which is presented today is not 100% accurate even though it is efficient.
An Efficient Algorithm for Co-segmentation .
Clothing Co-segmentation for Recognizing People .
iCoseg: Interactive Co-segmentation on your iOS device (in short).
Implementation of Co-segmentation in MATLAB + C++ Code.
Foreground and background pixels.
The segmentation of each image will partition the set of pixel into foreground versus background pixels.
Our goal is to ensure that the foreground in the two images are similar.
We have seen an efficient algorithm, running in polynomial time (the running time of a max-flow algorithm).
Texture feature vector.
Cut(A,B) is sum of weights with one end in A and one end in B ,we want to minimize the cut cost.
Assoc(A,V) is sum of all edges with one end in A , we want to maximize the sum of all weights for every A,B element in the partition
Feature vector based on texture segmentation:
We can use spatial filter for where the DOOG filters at various scales and orientations .
The color encoding system used for analog television worldwide (NTSC, PAL and SECAM). The YUV color space (color model) differs from RGB, which is what the camera captures and what humans view.
The Y in YUV stands for "luma," which is brightness.
U and V provide color information and are "color difference" signals of blue minus luma (B-Y) and red minus luma (R-Y)
The same day mutual information maps are reflected (symmetry is assumed) , summed and thresholded (by a value constant across image collection) to yield clothing masks that appear remarkably similar across collections.
The identity recognition and clothing segmentation problems are inter-twined; a good solution for one aides in the solution for the other.
Multiple images of the same person improves clothing segmentation.
Person recognition improves with improvements to the clothing segmentation.
Dorit S. Hochbaum, Vikas Singh. An efficient algorithm for co-segmentation. ICCV Oct 2009 , Kyoto, Japan, the 12th IEEE Conference on Computer Vision.
A. C. Gallagher and T. Chen. Clothing cosegmentation for recognizing people. In Proc. of Conf. on Computer Vision and Pattern Recognition, 2008.
Slides Credit: Jad Silbak