Sparse representations based attribute learning for flower classification

Abstract Classification for flowers is a very difficult task. Traditional methods need to built a classifier for each flower category, and obtain large number of flower samples to train these classifiers. In practice, many different types of flowers make the job become very difficult and boring. In this work, we present an attribute based approach for flowers recognition. Particularly, instead of training for a specific category of flowers directly based on manually designed features such as SIFT and HoG, we extract a series of visual attributes from a given set of flower images and generalize these to new images with possibly unknown flowers. A recently proposed sparse representations classification scheme is employed to predict the attributes of a given flower image from any category. In addition, we use a genetic algorithm to find the most discriminative attributes among others for better performance during the stage of flower classification. The effectiveness of the proposed method is validated on a publicly available flower classification database with promising results.