Flower Classification Using Neural Network Based Image Processing
In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance. The database of flower images is a mixture of images taken from World Wide Web and the images taken by them. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only.