Flower classification using fusion descriptor and SVM

This paper aims to develop an effective flower classification approach using the technology of feature extraction. With this regard, a fused descriptor based on Pyramid Histogram of Visual Words (PHOW) is used to extract the color, texture and contour information of flower image. Secondly, Dictionary Learning and Locality-constrained Linear Coding (LLC) are operated on PHOW feature and then images are presented by means of Spatial Pyramid Pooling (SPP). Finally, the integrated feature is sent to Support Vector Machines (SVM) classifier for training and testing. By conducting an experiment study in Oxford 17 dataset, our proposed method provided a satisfactory result with an overall 86.17% accuracy rate.