Curriculum Vitae

Research Interests

Part-based approaches for Fine-grained Visual Categorization

Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts. Hence, we research different part estimation approaches and part-classification methods. Besides the part estimation and part-based classification approaches, we aim to go a step further and decide which of the estimated parts contribute the most to the final classification. We call this approach the Active Part Selection because the decision should be made actively based on previously known information, like initial classification result, previously selected parts or the uncertainty of the classifier. From our point of view, Active Part Selection separates into three different steps: part estimation, part-based classification, and the actual part selection process. Hence, our work focuses on these aspects.