A mission-oriented citizen science platform for efficient flower classification based on combination of feature descriptors

Abstract

This paper describes a citizen science system for flora monitoring that employs a concept of missions, as well as an automatic approach for flower species classification. The proposed method is fast and suitable for use in mobile devices, as means to achieve and maintain high user engagement. Besides providing a web-based interface for visualization, the system allows the volunteers to use their smartphones as powerful sensors for collecting biodiversity data in a fast and easy way. The classification accuracy is increased by a preliminary segmentation step that requires simple user interaction, using a modified version of the GrabCut algorithm. The proposed classification method obtains good performance and accuracy, by combining traditional color and texture features together with carefully designed features, including a robust shape descriptor to capture fine morphological structures of the objects to be classified. A novel weighting technique assigns different costs to each feature, taking into account the inter-class and intra-class variation between the considered species. The method is tested on the popular Oxford Flower Dataset, containing 102 categories and we achieve state-of-the-art accuracy while proposing a more efficient approach than previous methods described in the literature.