Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained object classification. The contributions of the thesis are three-fold. First, we introduce two databases of high-resolution images of arthropod specimens we collected to promote the development of highly accurate fine-grained recognition methods. Second, we give a literature review on the development of Bag-of-words (BOW) approaches to image classification and present the stacked evidence tree approach we developed for the fine-grained classification task. We draw connections and analyze differences between those two genres of approaches, which leads to a better understanding about the design of image classification approaches. Third, benchmark results on our two datasets are pre- sented. We further analyze the influence of two important variables on the performance of fine-grained classification. The experiments corroborate our hypotheses that a) high resolution images and b) more aggressive information extraction, such as finer descriptor encoding with large dictionaries or classifiers based on raw descriptors, is required to achieve good fine-grained categorization accuracy. / Graduation date: 2013