Product Image Classification Based On Multi-feature Fusion And Deep Learning Algorithm

With the rapid development of Internet technology,online shopping has become a trend and trends,more and more people choose to select and buy goods in Jingdong,Taobao and other e-commerce platform.Major e-commerce platform in order to meet the needs of users,more and more new service has be launched,the function of product recommendation is one of them.In order to manage and classification of goods better,and according to users browsing history to push related products,product image classification techniques have been proposed,but also become a major research focus at present.In some of the early studies,scholars were used the methods of manual classification or image classification method based on text content,which not only requires a lot of manpower and material resources,and the classification accuracy has yet to achieve good results.In view of such problems,the researchers have proposed classification method based on image content,Content-based image classification method is based on the relevant characteristics of the image content having feature extraction,and then accordance with these characteristics to classify the image.Currently,commonly used in image content feature extraction methods are mainly based on low-level visual features of images,such as color,texture,shape and Related spatial relationship,etc.This method has been significantly improved over the previous classification,Accordingly,the image classification method based on image content is still the mainstream of research field."Product Image Classification Based on Multi-feature Fusion and Deep Learning Algorithm",Using the content of the image to extract features,in order to obtain a more nature description of the image content.Multi-feature fusion algorithm is use the basic visual information on the image to extract features,including image color features,texture features and shape features,then use multi-feature fusion algorithm to fuse these features.This information of the multi-feature fusion will be the data as the input layer of the Deep Belief Network,the model of Deep Belief Network will use this data for training and classification.With the process of the learning and classification based on the model of deep learning,using BP algorithm to update the weights of the model,to improve the classification accuracy of the algorithm.Finally,we use the Jingdong product image library to test and analysis "Product Image Classification Based on Multi-feature Fusion and Deep Learning Algorithm",The results of this algorithm comparing with the experimental results of using single feature algorithm,it is concluded that by using single feature algorithm to classify the test sample library,the classification accuracy obtained around 60%,and this article proposed the use of multiple features fusion classification algorithm to the classification results to 82.3%,at the same time to ensure the operation timely;In order to be more comprehensive to validate the effectiveness of the algorithm,The results of this algorithm comparing with several other major classification algorithm,the results show that the algorithm is compared with other mainstream algorithm has obvious advantages of classification accuracy,and its average processing time per image compared with other algorithms do not have obvious difference.In summary,the proposed algorithm for the test sample library classification,in terms of accuracy and timeliness have better results.