Flower classification based on local and spatial visual cues
Wenjing Qi, Xue Liu, Jing Zhao · 2012
This paper addresses flower image classification. The extent of blossom, deformation and inter-class appearance blur of flowers add great difficulties to flower classification task in addition to view, color, illumination changes that commonly occurred in other objects classification tasks. In this paper, SIFT-like feature descriptors and feature context method are used in coding local and spatial information, then LibLinear SVM classifier is employed for classification. Experimental results show that CSIFT is more robust and stable than SIFT and Dense SIFT in representing flower image. The accuracy of classification with CSIFT and feature context is comparable to state-of-the-art method. Since we do not need segment flower out of image in advance, practically, our method is better in performance and efficiency.