Image Segmentation Using Higher-Order Correlation Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sungwoong KimPushmeet Kohli

Abstract

In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation. It derives its partitioning result from a pairwise graph by optimizing a global objective function such that it simultaneously maximizes both intra-cluster similarity and inter-cluster dissimilarity. In the HO-CC, the pairwise graph which is used in the CC is generalized to a hypergraph which can alleviate local boundary ambiguities that can occur in the CC. Fast inference is possible by linear programming relaxation, and effective parameter learning by structured support vector machine is also possible by incorporating a decomposable structured loss function. Experimental results on various data sets show that the proposed HO-CC outperforms other state-of-the-art imag...Continue Reading

References

Aug 25, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pablo ArbeláezJitendra Malik
Sep 26, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Sungwoong KimChang D Yoo

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Citations

Jul 23, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Ting LiuTolga Tasdizen
Aug 16, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Zhuwen LiKim-Chuan Toh
Jan 15, 2021·Sensors·Bowen LiuZhilin Zhang

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