Dictionary-Free Categorization of Very Similar Objects via Stacked Evidence Trees

Current work in object categorization discriminates among objects that typically possess gross differences which are readily apparent. However, many applications require making much finer distinctions. We address an insect categorization problem that is so challenging that even trained human experts cannot readily categorize the insects based on their images. The state of the art that uses visual dictionaries, when applied to this problem, yields mediocre results (16.1% error). Three possible explanations for this are (a) the dictionaries are unsupervised, (b) the dictionaries lose the detailed information contained in each keypoint, and (c) these methods rely on hand-engineered decisions about dictionary size. This paper presents a novel, dictionary-free methodology. A random forest of trees is first trained to predict the class of an image based on individual keypoint descriptors. A unique aspect of these trees is that they do not make decisions but instead merely rec...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Andrew Moldenke, Asako Yamamuro, David A. Lytle, Eric N. Mortensen, Gonzalo Martínez-Muñoz, Linda G. Shapiro, Nadia Payet, Natalia Larios Delgado, Robert Paasch, Sinisa Todorovic, Thomas G. Dietterich, Wei Zhang
• Keywords: Computer Vision, CVPR 2009, Similar Objects, Stacked Evidence Trees