Flower Image Classification Based On Deep Model Transfer Learning

Image classification is one of the important research directions in the field of artificial intelligence and pattern recognition.Flower image classification is to classify the main flowers in images.It has important application value in research and protection of plant species and intelligent management of garden flowers and plants.Because of the huge number of categories,small differences between classes,large differences within the category,the complex background,and the small number of samples,it is often difficult to classify flowers accurately.Based on the characteristics of flower image data and the existing problems of classification algorithm,guided by the theory of deep learning,transfer learning,and multitask learning,flower image classification is studied in this thesis.The main work is as follows:(1)Aiming at overcoming difficulties of designing feature description operator,weak ability of feature extraction;and solving the problems of complex structure,large parameter scale,difficult fitting small datasets of deep model,a fine grained image classification method of deep learning integrated with transfer learning is proposed.Firstly,after pre-training on the coarse-grained image dataset,the parameter distribution of deep model was able to extract natural image features.Secondly,the deep model is locally trained to transfer on fine-grained image datasets.The experimental results show that the classification accuracy rate reaches 96.27% on 102 kinds of flower image datasets.The method also obtained 72.23%,73.33%,86.00%,and 89.72% classification accuracy on the 120 class dog,200 class bird,37 class cat and dog,and 196 class car image datasets,respectively.(2)Deep model training often requires a very large dataset size,a large number of differences in characteristics to meet the large parameter scale in the model,and the fine-grained image dataset scale is much smaller than the coarse-grained image dataset size.Modifying the original data in different ways can increase the data,but the introduced difference features are not obvious.In response to this problem,this paper proposes an external data augmentation method based on WEB crawlers.Firstly,indiscriminately crawl images from the social platforms,search engines(such as Instagram,Flickr,Google,Bing,Baidu)according to Hashtag,Keyword search results which inevitably contains a large number of redundant images.Then,the redundant images are filtered and organized to generate augmented datasets.Finally,usethe augmented dataset instead of the original dataset to train the model.The experimental results show that the classification accuracy rate reaches 99.41% on 102 kinds of flower image datasets,which is 3.14%higher than the original dataset.Compared to the original dataset,the use of external augmented data and can effectively improve the classification accuracy.(3)According to the actual needs,design and implementation of a floral image classification prototype system based on B/S architecture.Firstly,export the transferred model,freeze parameters,and deploy online.Then,remotely call the model on the server through the browser for image classification,and give the classification result on the WEB side.On 6 mobile terminals.the test results showed that out of the 18 flower images,only 2 results were located in Top-2,and the others were Top-1.Without considering the network delay,the server response speed is controlled within 40 ms.Prove that this system has high accuracy and immediacy.