Heterogeneous Feature Machines for Visual Recognition

With the recent efforts made by computer vision researchers, more and more types of features have been designed to describe various aspects of visual characteristics. Modeling such heterogeneous features has become an increasingly critical issue. In this paper, we propose a machinery called the Heterogeneous Feature Machine (HFM) to effectively solve visual recognition tasks in need of multiple types of features. Our HFM builds a kernel logistic regression model based on similarities that combine different features and distance metrics. Different from existing approaches that use a linear weighting scheme to combine different features, HFM does not require the weights to remain the same across different samples, and therefore can effectively handle features of different types with different metrics. To prevent the model from overfitting, we employ the so-called group LASSO constraints to reducemodel complexity. In addition, we propose a fast algorithm based on co-ordin...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Liangliang Cao, Jiebo Luo, Feng Liang, Thomas S. Huang
• Keywords: Computer Vision, Heterogeneous Feature Machines, ICCV 2009, Visual Recognition