CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2014 vol.36 Issue No.09 - Sept.
Issue No.09 - Sept. (2014 vol.36)
Pushmeet Kohli , Machine Learning and Perception , Microsoft Research Cambridge, 7 JJ Thomson Ave, Cambridge, United Kingdom
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 image segmentation algorithms. The HO-CC framework is therefore an efficient and flexible image segmentation framework.
Image segmentation, Clustering algorithms, Image edge detection, Vectors, Correlation, Inference algorithms, Partitioning algorithms,structural learning, Image segmentation, correlation clustering
Pushmeet Kohli, "Image Segmentation UsingHigher-Order Correlation Clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 9, pp. 1761-1774, Sept. 2014, doi:10.1109/TPAMI.2014.2303095