Objective

2

Flower image classification is a fine-grained image classification. Its main challenges are large intra-class differences and inter-class similarities. Different types of flowers have high similarities in morphology

color

and other aspects

whereas flowers in the same category have great diversities in color

shape

and others. According to research and analysis

the current methods of flower image classification can be divided into two categories:methods based on handcrafted features and methods based on deep learning. The former usually obtains flower areas by image segmentation methods and then extracts or designs features manually. Finally

the extracted features are combined with a traditional machine learning algorithm to complete classification. These methods rely on the design experience of researchers. By contrast

methods based on deep learning utilize deep networks to learn the features of flowers automatically. Bounding boxes and part annotations are used to define accurate target positions

and then different convolution neural network models are fine-tuned to obtain the targets' features. Given that currently available flower image datasets lack annotation information

such as bounding box and part annotation

these strongly supervised methods are difficult to apply. Furthermore

tagging many flower images' bounding boxes and part annotations incurs high cost. To solve these problems

this study proposes an unsupervised flower image classification method on the basis of selective convolution descriptor aggregation.

Method

2

A flower image classification network is constructed on the basis of selective deep convolution descriptor aggregation. The proposed method can be divided into four phases:flower image preprocessing

selection and aggregation of convolution features in the Pool5 layer

selection and aggregation of convolution features in the Relu5-2 layer

and multi-layer feature fusion and classification. In the first phase

flower images are preprocessed with the normalization method that retains the aspect ratio to make the size of all flower images equal; thus

the dimension of each flower feature generated by the deep convolutional neural network is consistent. The input image size is set to 224×224 pixels in this study. In the second phase

the features of the preprocessed flower images are learned by VGG-16

which is the deep convolutional neural network model pre-trained by ImageNet. Then

the saliency region is located according to the high response value in the feature map of the Pool5 layer. However

some background regions also have high response values. The area of the background region with a large response value is smaller than the target area. Thus

the flood filling algorithm is used to calculate the maximum connected region of the saliency region. On the basis of the location information of the saliency region

deep convolution features within the region are selected and aggregated to form a low-dimensional feature of flower images. In the third phase

deep convolution features in the Relu5-2 layer are selected and fused to form another low-dimensional feature of flowers. Multi-layer convolution features have been proven to help the network to learn features and then complete the classification task; thus

the deep convolution features in the Pool5 and Relu5-2 layers are chosen in this study. Similarly

a saliency region map from the Relu5-2 layer is obtained on the basis of the response value. The saliency map from the Relu5-2 layer more accurately locates the flower region relative to the saliency map from the Pool5 layer

in which numerous noise regions and few semantic information exist. Thus

the saliency region map from the Relu5-2 layer is combined with a maximum connected region map from the Pool5 layer to produce a true saliency region map with little noise. Finally

deep convolution features are selected and aggregated to form the low-dimensional feature of flower images from the Relu5-2 layer on the basis of the location information of the true saliency region map. In the final phase

the above two low-dimensional features are aggregated to form the final flower features

which are then entered into the softmax layer for classification.

Result

2

To explore the effects of the proposed selective convolution descriptor aggregation method

we perform the following experiment on Oxford 102 Flowers. The preprocessed flower images are entered into the AlexNet

VGG-16

and Xception models

all of which are pre-trained by ImageNet. Experimental results show that the classification accuracy of the proposed method is superior to that of other models. Experiments are also conducted to compare the proposed method and other current flower image classification methods in the literature. Results indicate that the classification accuracy of this method is higher than that of methods based on handcrafted features and other methods based on deep learning.

Conclusion

2

A method for classifying flower images using selective convolution descriptor aggregation was proposed. A flower image's features were learned by using the transfer learning technique on the basis of a pre-trained network. Effective deep convolution features were selected according to the response value distribution in the feature map. Then

multi-layer deep convolution features were fused. Finally

the softmax layer is used for classification. The advantages of this method include locating the conspicuous region in the flower image in an unsupervised manner and selecting deep convolution features in the located region to exclude other invalid parts

such as background and noise parts. Therefore

the accuracy of flower image classification can be improved by reducing the disturbing information from invalid parts.