Structure-perceptron learning of a hierarchical log-linear model

In this paper, we address the problems of deformable object matching (alignment) and segmentation with cluttered background. We propose a novel hierarchical log-linear model (HLLM) which represents both shape and appearance features at multiple levels of a hierarchy. This model enables us to combine appearance cues at multiple scales directly into the hierarchy and to model shape deformations at short-range, medium range, and long-range. We introduce the structure-perceptron algorithm to estimate the parameters of the HLLM in a discriminative way. The learning is able to estimate the appearance and shape parameters simultaneously in a global manner. Moreover, the structureperceptron learning has a feature selection aspect (similar to AdaBoost) which enables us to specify a class of appearance/shape features and allow the algorithm to select which features to use and weight their importance. This method was applied to the tasks of deformable object localization, segmentation, matching ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Long Zhu, Yuanhao Chen, Xingyao Ye, Alan L. Yuille
• Keywords: Appearance Cues, Computer Vision, CVPR 2008, Deformable Object Localization, Hierarchical Log-linear Model, Shape Deformations, Shape Parameters