FLOWER MULTI-CLASS CLASSIFICATION USING
CONVULTIONAL NEURAL NETWORKS
Nadine Brown
Abstract: Deep learning, particularly convolutional neural networks (CNN) have become a popular technique
in image classification. In this study, the author uses CNNS to implement and deploy a deep learning based
custom image multi-class classifier.
Abstract
Currently, the classification of flower species has become a hot topic in the field of
image classification. Flower classification belongs to the category of fine image
classification, and such images are usually represented by multiple visual features.
At present, all the flower classification methods based on a single convolutional
neural network (CNN) model can hardly extract the features of a flower image as
much as possible. In view of the limitation of description methods for flower
features and the problem of low accuracy of flower species recognition, this paper
proposes a flower classification framework based on ensemble of CNNs. The
method consists of the following three parts: (1) The same flower image is
processed differently to make the color, texture and gradient of the flower image
more prominent; (2) Fine-tune the structure and parameters of the convolutional
neural network to adapt it to the extraction of corresponding features. Then use
the CNN model with different characteristics to extract the corresponding
features; and (3) A framework that can fuse each CNN sub-learner is used to
combine various features effectively. We tested the effectiveness of our method on
the Oxford Flowers 102 Dataset [2]. The result demonstrates that the proposed
approach effectively improves the accuracy of flower classification.