Research Of Android Image Classification System

In recent years, with the rapid development of smart phones, the need of smart phone’s function is becoming more and more diverse. Taking pictures is an important function among them. The photos taken by smart phones can be used in avatar, social network sharing and other areas. Image classification can help smart phone to set parameters such as aperture and shutter automatically, improve the quality of pictures.In order to meet the demand, this paper researches the technology of image classification on smartphones, implementing the Android image scene classification system. This paper introduces image classification technologies based on color, texture, shape and spatial features. Due to Android phone’s processing speed and memory limit, CEDD which combines the color and texture features was choosed as the method of extracting image information, it keeps a good balance between features’ size and retrieval quality. The system extacts CEDD descriptors after taking picture, then classifies the photo into one of the six categories whitch include landscape, people, night, backlit, macro and text by one to one SVM method. In the classification prcessing, this paper rewrite the code of training model and voting in predict function from libsvm, then move it to Android mobile phone.This paper introduces the implementation process of classification system. The system has four modules, including interface module, NDK interface module, feature extracting module and classification jugement module. They work on Android operating system with the programming environment of Java and C. By study of the development of the Android NDK, this paper achieve the feature extraction and SVM classification by establishing a connection between the system upper application and dynamic link library from compiled C files. In this system, the user will get the result of classification through processing after taking a picture.Finally, the performance of the implementation of the image scene classification system was tested. The results show that the system has a good performance.