| With the popularity of intelligent device and the development of network,the number of images is increasing rapidly.Facing so much image data how users find out the one directly would be an important issue.In the past,images were organized by manual tagging,but when considering enormous pictures it would waste lots of time and manpower.That is why people improve the way to categorize by recording the date or location of the picture created.However,there are some limitations on this classification,because it is not designed base on the image content.It can be seen that these ways of managing pictures have been unable to satisfy the needs of users.Meanwhile,in recent years,with the development of artificial intelligence and the improvement of computer computing ability,“deep learning” has become the focus of attention and also been used in various fields,achieved great success in image recognition.Focusing on these problems,it is necessary to implement a system for automatic classification and labeling of pictures based on deep learning to help users improve efficiency.At present,there are many big internet companies own the image classification engines based on deep learning,but most of calculating rely on the server,which means it will occupy a lot of server resources,also limit the controllability of image classification.Therefore,this paper is going to design and implement an image auto classification application,which based on the neural network model of deep learning theory.In this system,image classification could be processed in browser,not only reduces the computing pressure in server,but also make the system more controllable and more flexible.This paper first expounds the basic principle and algorithm of deep neural network,introduces the deep neural network model and related technology framework.Then,three key modules-Data generator,Model trainer and Image classifier-are described in detail from the requirement analysis to the design and implementation.In the data generator module,the original image data is transformed into a binary file for training.In the training module,the output structure of the original network model is modified to apply to different data classes.In the classifier module,the weight parameters are loaded to classify the images in the browser.To implement the final function,firstly,train the deep neural network model by using the existing image data,and save the weight parameters to file.Secondly,build a network server for users to achieve a series of basic functions for picture management.In addition,the image classifier insert into browser can achieve the functionality of image auto-tagging by calling the weight parameters file.Finally,through analysis of experiment data,the system is stable,functional,and able to satisfy the needs of practical applications.Up to now,the system achieved the above key modules,and provides a complete solution include user management,image management and label management,meanwhile the system can be developed to other image classification system by different classification needs,which makes user to manage personal image resources more convenient and quick. |
