A Review on Flower Classification Using Neural Network Classifier

Image processing plays an important role in extracting useful information from images. However, the process of translating an image into a statistical distribution of low-level features is not an easy task. These tasks are complicated since the acquired image data often noisy and target objects are influenced by lighting, intensity or illumination. In this paper we have present literature survey of various methods 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), discrete wavelet transform (DWT) and Color features such as normalized color histogram. A flower image is segmented using a threshold based method. The database has different flower images with similar appearance.