Optimizing Complex Loss Functions in Structured Prediction

Abstract. In this paper we develop an algorithm for structured prediction that optimizes against complex performance measures, those which are a function of false positive and false negative counts. The approach can be directly applied to performance measures such as Fβ score (natural language processing), intersection over union (image segmentation), Precision/Recall at k (search engines) and ROC area (binary classifiers). We attack this optimization problem by approximating the loss function with a piecewise linear function and relaxing the obtained QP problem to a LP which we solve with an off-the-shelf LP solver. We present experiments on object class-specific segmentation and show significant improvement over baseline approaches that either use simple loss functions or simple compatibility functions on VOC 2009.

• Type: Conference
• Year: 2010
• Where: ECCV
• Keywords: Computer Vision, ECCV 2010, False Negative Counts, Loss Functions, Performance Measures