HOPS: Efficient region labeling using Higher Order Proxy Neighborhoods

We present the Higher Order Proxy Neighborhoods (HOPS) approach to modeling higher order neighborhoods in Markov Random Fields (MRFs). HOPS incorporates more context information into the energy function in a recursive and cached manner. It induces little or no additional computational cost in the overall minimization process, and can better represent the underlying energy leading to fewer total computations. Indeed, when integrated with the Graph-Shifts energy minimization algorithm we observe a 30% average decrease to the convergence time. We apply HOPS to high-level labeling of natural and geospatial images; our results show that HOPS leads to smoother labelings that better follow object boundaries. HOPS can label an image with an average 75% accuracy in a couple of seconds.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Albert Y. C. Chen, Jason J. Corso, Le Wang
• Keywords: Average 75% Accuracy, Computer Vision, Graph-Shifts Energy Minimization, ICPR 2008, Order Proxy Neighborhoods