Fusing well-crafted feature descriptors for efficient fine-grained classification

Abstract

As citizen science projects become more popular and engage an increasing number of volunteers, smartphones are turning into commonly used sensors in the biodiversity environment. In this paper, we propose a novel approach for classification of subordinate categories such as plant and insect species that is fast and suitable for use in mobile devices. In particular, we show that a combination of carefully designed features, including a robust shape descriptor to capture fine morphological structures of objects, as well as traditional color and texture features, is essential for obtaining good performance. A novel weighting technique assigns different costs to each feature, taking into account the inter-class and intra-class variation between species. We tested our proposed method in the popular Oxford Flower Dataset and in the Leeds Butterfly Dataset. We are able to achieve state-of-the-art accuracy while proposing an efficient approach that is suitable for mobile applications and can be applied to different species.