Learning Domain-Specific Feature Descriptors for Document Images
Kandan Ramakrishnan, Evgeniy Bart · 2012
Many machine learning algorithms rely on feature descriptors to access information about image appearance. Using an appropriate descriptor is therefore crucial for the algorithm to succeed. Although domain- and task-specific feature descriptors may result in excellent performance, they currently have to be hand-crafted, a difficult and time-consuming process. In contrast, general-purpose descriptors (such as SIFT) are easy to apply and have proved successful for a variety of tasks, including classification, segmentation, and clustering. Unfortunately, most general-purpose feature descriptors are targeted at natural images and may perform poorly in document analysis tasks. In this paper, we propose a method for automatically learning feature descriptors tuned to a given image domain. The method works by first extracting the independent components of the images, and then building a descriptor by pooling these components over multiple overlapping regions. We test the proposed method on several document analysis tasks and several datasets, and show that it outperforms existing general-purpose feature descriptors.