Abstract: In recent years,image multi-classification task and deep learning have received increasingly attentions,and multi-classification image data framework based on convolutional neural network(MIF-CNN) has also been widely used.Traditional CNN-based multi-class image data learning generally has a problem that the image processing is complicated,the feature dimensions are large,and the time complexity is high.To solve this problem,this paper proposes a multi-classification image data framework based on CNN with intersect features(MIF-CNNIF).MIF-CNNIF is a framework for performing multi-classification tasks based on intersect features obtained by multiple feature selection algorithms.Through extensive comparative experiments on 10 multi-class image data sets,the results validate the effectiveness of MIF-CNNIF.The contributions of MIF-CNNIF are that,1)it avoids the problem of setting too many parameters with the usage of pre-trained CNN models;2)it keeps features dimension and time cost after comparing with MIF-CNN;3)it has a better average recognition accuracy than MIF-CNN;4)the effectiveness of combined feature algorithms is verified on multi-class image data sets.

Key words: Convolutional neural network, Feature selection, Intersect features, Image multi-class, Combine feature