Interactive Co-Segmentation Using Histogram Matching and Bipartite Graph Construction.

Date of Submission

December 2017

Date of Award

Winter 12-12-2018

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Electronics and Communication Sciences Unit (ECSU-Kolkata)


Chanda, Bhabatosh (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

Co-segmentation is defined as jointly partitioning multiple images having same or similar objects of interest into foreground and complementary part is mapped as background.In this thesis a new interactive co-segmentation method using a global energy function and a local smooth energy function with the help of histogram matching is being proposed. The global scribbled energy takes the help of histograms of the regions in the image to be co-segmented and the user scribbled images to estimate the probability of each region belonging either to foreground or background region. The local smooth energy function helps in estimating the probability of regions having similar colour appearance.To further improve the quality of the segmentation, bipartite graph is constructed using the segments. The algorithm has been implemented on iCoseg and MSRC benchmark data sets and the experimental results show significant good results compared to many state-of-the-art unsupervised co-segmentation and supervised interactive co-segmentation methods.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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